Static Point Research Articles

Unsupervised Clustering-based 3D Static Scene Construction Using LiDAR Channel and Azimuth Angle

Abstract. Cameras are typically used at road intersections to collect data to perform object detection but struggle in low-light and harsh weather. On the other hand, Light Detection and Ranging (LiDAR) is used as a key technology in 3D vision systems. It gives the 3D point cloud, which includes accurate depth information, but its resolution is cost-dependent, with higher resolutions being more expensive. Deep learning-based method requires large, labelled dataset which increases the cost, time and accuracy depending on the model trained on labelled dataset. To perform object detection in point cloud data is a challenging task due to the incomplete representations, data sparsity and unavailability of training data. To overcome this by using an unsupervised approach, it is important to identify the static scene and then detect the moving object. This work incorporated a novel approach to construct static scene using azimuth angle and laser channel information using an unsupervised clustering approach. This work incorporates two modules I) data collection using VLP-16 LiDAR, and II) static scene construction using DBSCAN (density-based spatial clustering and noise) clustering-based approach. Data is collected at a 4-legged intersection and pre-processed to extract aggregated distances corresponding to the unique pair of azimuth angle and laser channel. DBSCAN is used to perform clustering on the aggregated distances, based on the highest silhouette score and lowest intra distance between points in cluster, static points are identified, and static scene constructed. The qualitative evaluation of method demonstrates that the algorithm effectively and accurately filters out background points.

ADS–SLAM: a semantic SLAM based on adaptive motion compensation and semantic information for dynamic environments

Abstract Static environment assumptions are a prerequisite for simultaneous localization and mapping (SLAM), while interference from dynamic objects in the environment can seriously impair the system’s localization accuracy. Recently, many works have combined deep learning and geometric constraints to attenuate the interference of dynamic objects, but poor real-time performance and low accuracy in high dynamic scenes still exist. In this paper, we propose a semantic SLAM algorithm for complex dynamic scenes named ADS–SLAM. Our system combines the advantages of semantic information and motion constraints to remove dynamic points during tracking and localization. First, an adaptive dynamic point detection method based on epipolar constraint between adjacent frames is designed to adapt to the changes of object motion states and a motion area detection method based on Gaussian mixture model and Kalman Filter is utilized to effectively compensate the missed motion areas. Second, an object detection network with improved inference in the backend is utilized to extract prior object semantics. Lastly, the multi-level information is integrated in order to comprehensively screen all dynamic points in the environment and utilize only static points for pose estimation and optimization. Experimental evaluations on challenging public datasets and outdoor dynamic environments demonstrate that our algorithm achieves high localization accuracy in almost all dynamic scenarios compared to the current state-of-the-art SLAM algorithms, with the highest accuracy in high dynamic scenarios, and shows real-time performance for practical applications.

YPR-SLAM: A SLAM System Combining Object Detection and Geometric Constraints for Dynamic Scenes

Traditional SLAM systems assume a static environment, but moving objects break this ideal assumption. In the real world, moving objects can greatly influence the precision of image matching and camera pose estimation. In order to solve these problems, the YPR-SLAM system is proposed. First of all, the system includes a lightweight YOLOv5 detection network for detecting both dynamic and static objects, which provides pre-dynamic object information to the SLAM system. Secondly, utilizing the prior information of dynamic targets and the depth image, a method of geometric constraint for removing motion feature points from the depth image is proposed. The Depth-PROSAC algorithm is used to differentiate the dynamic and static feature points so that dynamic feature points can be removed. At last, the dense cloud map is constructed by the static feature points. The YPR-SLAM system is an efficient combination of object detection and geometry constraint in a tightly coupled way, eliminating motion feature points and minimizing their adverse effects on SLAM systems. The performance of the YPR-SLAM was assessed on the public TUM RGB-D dataset, and it was found that YPR-SLAM was suitable for dynamic situations.

Steady morphokinetic progression is an independent predictor of live birth: a descriptive reference for euploid embryos.

Can modelling the longitudinal morphokinetic pattern of euploid embryos during time-lapse monitoring (TLM) be helpful for selecting embryos with the highest live birth potential? Longitudinal reference ranges of morphokinetic development of euploid embryos have been identified, and embryos with steadier progression during TLM are associated with higher chances of live birth. TLM imaging is increasingly adopted by fertility clinics as an attempt to improve the ability of selecting embryos with the highest potential for implantation. Many markers of embryonic morphokinetics have been incorporated into decision algorithms for embryo (de)selection. However, longitudinal changes during this temporal process, and the impact of such changes on embryonic competence remain unknown. Aiming to model the reference ranges of morphokinetic development of euploid embryos and using it as a single longitudinal trajectory might provide an additive value to the blastocyst morphological grade in identifying highly competent embryos. This observational, retrospective cohort study was performed in a single IVF clinic between October 2017 and June 2021 and included only autologous single euploid frozen embryo transfers (seFET). Reference ranges were developed from [hours post-insemination (hpi)] of the standard morphokinetic parameters of euploid embryos assessed as tPB2, tPNa, tPNf, t2-t9, tSC, tM, tSB, and tB. Variance in morphokinetic patterns was measured and reported as morphokinetic variance score (MVS). Nuclear errors (micronucleation, binucleation, and multinucleation) were annotated when present in at least one blastomere at the two- or four-cell stages. The blastocyst grade of expansion, trophectoderm (TE), and inner cell mass (ICM) were assessed immediately before biopsy using Gardner's criteria. Pre-implantation genetic diagnosis for aneuploidy (PGT-A) was performed by next-generation sequencing. All euploid embryos were singly transferred in a frozen transferred cycle and outcomes were assessed as live birth, pregnancy loss, or not pregnant. Association of MVS with live birth was investigated with regression analyses. TLM data from 340 seFET blastocysts were included in the study, of which 189 (55.6%) resulted in a live birth. The median time for euploid embryos to reach blastulation was 109.9 hpi (95% CI: 98.8-121.0 hpi). The MVS was calculated from the variance in time taken for the embryo to reach all morphokinetic points and reflects the total morphokinetic variability it exhibits during its development. Embryos with more erratic kinetics, i.e. higher morphokinetic variance, had higher rates of pregnancy loss (P = 0.004) and no pregnancy (P < 0.001) compared to embryos with steadier morphokinetic patterns. In the multivariable analysis adjusting for ICM, TE grade, presence of nuclear errors, and time of blastulation, MVS was independently associated with live birth (odds ratio [OR]: 0.62, 95% CI: 0.46-0.84, P = 0.002) along with ICM quality. Live birth rate of embryos with the same ICM grading but different morphokinetic variance patterns differed significantly. Live birth rates of embryos exhibiting low MVS with ICM grades A, B, and C were 85%, 76%, and 67%, respectively. However, ICM grades A, B, and C embryos with high MVS had live birth rates of 65%, 48%, and 21% (P < 0.001). The addition of the MVS to embryo morphology score (ICM and TE grading) significantly improved the model's AUC value (0.67 vs 0.62, P = 0.015) and this finding persisted through repeat cross-validation (0.64 ± 0.08 vs 0.60 ± 0.07, P < 0.001). The exclusion of IVF cases limits, for now, the utility of the model to only ICSI-derived embryos. The utility of these reference ranges and the association of MVS with various clinical outcomes should be further investigated. We have developed reference ranges for morphokinetic development of euploid embryos and a marker for measuring total morphokinetic variability exhibited by developed blastocysts. Longitudinal assessment of embryonic morphokinetics rather than static time points may provide more insight about which embryos have higher live birth potential. The developed reference ranges and MVS show an association with live birth that is independent of known morphological factors and could emerge as a valuable tool in prioritizing embryos for transfer. This study received no external funding. The authors declare no conflicting interests. N/A.

Development of a thermal diagnostic system for the SPARC tokamak.

The SPARC tokamak will have scrape-off layer parallel heat fluxes on the order of GW/m2. Managing power exhaust of this magnitude will be mandatory for a reactor-scale device. To enable this mission, a thermal diagnostic suite will be deployed to measure the in-vessel structural temperatures to ensure they do not exceed their design limits and to determine the spatial distribution and magnitude of energy deposited onto the first wall. Thermocouples and fiber Bragg gratings have been selected for their environmental compatibility and proven useful on other fusion devices. High-density thermocouple arrays in the divertor will have two spring-loaded thermocouples per divertor target tile, which are being used as calorimeters, and will look to resolve the temperature distribution within the tile due to a swept or static strike point. All systems will need to survive the vacuum vessel bake, set at a minimum plasma facing surface temperature of 350 °C, which presents a particularly challenging environment for the fiber-based subsystem. Along with this temperature design requirement, all the materials in the primary vacuum need to be ultra-high vacuum compatible, able to handle the expected neutron and gamma radiation, as well as tritium exposure, all of which restrict material options. Finally, due to the expected activated environment in SPARC, there will be little chance to replace defective sensors, so system resilience is ensured through toroidal redundancy, probe material selection, and mitigating the impact of common-mode failures. Initial testing of sensors show that intershot structural measurements are sufficiently captured with the raw output, but intrashot measurements of the plasma facing material requires model-based interpretive tools.

Dynamic and static nasolabial muscle anatomy of unilateral cleft lip adult patients based on magnetic resonance imaging data.

This study aims to obtain a three-dimensional reconstruction model based on magnetic resonance imaging (MRI) data of patients with different degrees of unilateral cleft lip and analyze the anatomy and changes in multiple groups of nasolabial muscles under dynamic and static conditions. One normal person and four adult patients with unilateral cleft lip were included, and MRI was performed under static (upper and lower lips closed naturally) and dynamic (pout and grin) conditions. 3D Slicer software was used to reconstruct the model and draw the anatomic morphology of nasolabial muscles. The distance between the junction (where the muscle merges into the orbicularis oris) of the levator muscle, zygomaticminor muscle, and zygomatic major muscle to the median sagittal plane, the starting point to the junction point, the dynamic and static junction points, and the angle between the connection of dynamic and static junctions and the horizontal plane were measured under three kinds of movements, and the ratio was calculated. In all patients, under dynamic and static conditions, the distance from the muscle junction to the median sagittal plane, their ratios of the cleft side to the non-cleft side were all greater than 1. While the ratio of the distance from the starting point of the muscle to the junction point is less than 1. At static conditions, the two ratios of the same muscle increased gradiently with the severity of the cleft, and the ratio of the zygomatic minor muscle was prominent in the same patient. The ratio of the cleft side to the non-cleft side was greater than 1, and the value for comparison was the angle of the line from the static to the dynamic junction and the horizontal plane. The symmetry of the insertion site of the orbicularis oris and the linear distance of both sides of the muscle are related to muscle and cleft types. The angle of muscle contraction on the cleft side is greater than that on the non-cleft side.

Assessments of GPS satellite orbiting period effects on diurnal and semi-diurnal luni-solar declinations utilizing Galileo satellites

Global Navigation Satellite Systems (GNSS) can observe a variety of surface deformations on Earth, including periodic oscillations at different frequencies. An example of such phenomena is ocean tide loadings (OTL), which result from the redistribution of water mass. The Global Positioning System (GPS) exhibits orbital geometry that causes its revisit and orbital periods to coincide with the diurnal and semi-diurnal luni-solar declination constituents, known as K1 and K2, respectively. Consequently, the system faces challenges in accurately estimating these periodic oscillations due to its orbital artifacts. This study aims to quantify the extent to which GPS orbital artifacts introduce periodic signals into the K1 and K2 constituents by utilizing the Galileo system and determining the most suitable positioning approach. A dataset from the International GNSS Service (IGS), spanning 40 days in 2024 and covering six stations, was analyzed. Coordinates were estimated using both kinematic positioning every 5 minutes and a 6-hour static precise point positioning (PPP) mode with a 3-hour shift. The power spectra for the east, north, and up components indicated that, on average, the GPS system contributes 52.8% to the K1 constituents and 66.3% to the K2 constituents. Despite expectations that the diurnal K1 and semi-diurnal K2 tidal constituents would be more prominent in the power spectra of the GPS comparing to that of natural signature or of other navigation system (Galileo for this study), the diurnal K1 tidal constituent appeared weak in the kinematic mode power spectra for the GPS system. These findings validate that the overlapped-static PPP mode is a more appropriate approach for estimating these periodic deformations.

Compressed Point Cloud Quality Index by Combining Global Appearance and Local Details

In recent years, many standardized algorithms for point cloud compression (PCC) has been developed and achieved remarkable compression ratios. To provide guidance for rate-distortion optimization and codec evaluation, point cloud quality assessment (PCQA) has become a critical problem for PCC. Therefore, in order to achieve a more consistent correlation with human visual perception of a compressed point cloud, we propose a full-reference PCQA algorithm tailored for static point clouds in this article, which can jointly measure geometry and attribute deformations. Specifically, we assume that the quality decision of compressed point clouds is determined by both global appearance (e.g., density, contrast, and complexity) and local details (e.g., gradient and hole). Motivated by the nature of compression distortions and the properties of the human visual system, we derive perceptually effective features for the above two categories, such as content complexity, luminance/geometry gradient, and hole probability. Through systematically incorporating measurements of variations in the local and global characteristics, we derive an effective quality index for the input compressed point clouds. Extensive experiments and analyses conducted on popular PCQA databases show the superiority of the proposed method in evaluating compression distortions. Subsequent investigations validate the efficacy of different components within the model design.

Effect of the Surface Peak-Valley Features on Droplet Impact Dynamics under Leidenfrost Temperature.

This study explores the kinetic behavior of droplets impacting microtextured surfaces under a Leidenfrost temperature, employing high-speed photography and picosecond laser micromachining techniques. The investigation focuses on two types of microtextured surfaces with totally different surface peak-valley features: a negatively skewed surface with micropit arrays (Ssk < 0) and a positively skewed surface with micropillar arrays (Ssk > 0). The results indicate that both microtextured surfaces contribute to a higher Leidenfrost temperature compared with the original smooth surface, which is consistent with previous studies. However, it is worth noting that the Leidenfrost points of the micropit and micropillar surfaces showed opposite trends with the microtexture area occupancy. Specifically, the Leidenfrost temperature on micropit surfaces increases with greater micropit area occupancy, while it decreases on micropillar surfaces under similar conditions, which is mainly attributed to the differential impact of area occupancy on droplet heat transfer efficiency. When the microtexture area occupancy is 50%, it is worth noting that the micropit and micropillar surfaces have nearly same roughness (Sa), but the Leidenfrost temperature was notably higher on the micropit surface with negative skewness (Ssk < 0), which was related to differences in vapor flow dynamics. We further find that the Weber number (We) significantly influences the Leidenfrost point, with the droplet impact wall behavior going through the states of film bounce back, ejecting tiny droplets and bounce back, and ultimately droplet breakup as the We increases. The dynamic Leidenfrost point was found to be generally higher than the static point and increases with the We. Finally, we compare the cooling efficiency of these surfaces, and it is found that the micropit surfaces with a negative skewness exhibit superior heat dissipation performance under the same conditions, which proved that the negatively skewed surface may have great potential in high-density heat dissipation technology.

Damage analysis of OC4 jacket subjected to cyclic loading by peridynamic approach

Catastrophic structural failure caused by fatigue damage under cyclic loads can be avoided by identifying critical locations of the damage initiation and the fatigue life during the design stage. Peridynamic (PD) theory defines structure as a collection of material points with non-local bond interactions where the structural discontinuity due to fatigue represented by instantaneous bond breakage is estimated through cumulative decrement of the bond’s life at each load cycle. In this work, we model a reference jacket presented under the Offshore Code Comparison Collaboration Continuation project (OC4) through peridynamic beam formulation. Initially, the static deformations of the beam and deformations of the OC4 jacket under static, harmonic, and irregular point loads are validated with ABAQUS results in all six degrees of freedom. Thereby, the PD fatigue parameters of the jacket’s steel material are calibrated from the experimental data of the corresponding material with a trial simulation for fatigue damage analysis. Later, different load cases from regular waves interacting with the jacket are generated in the PD framework by adopting linear wave theory. Based on the PD cyclic energy release rate model, a comparative study of all load cases to identify critical damage locations with the failure load cycles for damage initiation and fracture is performed for the considered OC4 jacket.

Large-scale multiple criteria group decision-making with information emendation based on unsupervised opinion evolutions

Large-scale multiple criteria group decision-making (MCGDM) is prevalent in diverse decision-making scenarios, involving numerous decision makers (DMs), the set of alternatives and criteria, and continuous temporal cycles. Opinions from DMs dynamically evolve through iterative interaction, leading to dynamic opinion evolutions. However, traditional MCGDM methodology usually establish the opinion formation on a static time point throughout information aggregation, which will lead to information distortion. This study develops a novel large-scale MCGDM method with information emendation based on an unsupervised opinion dynamics (UOD) model, combining with the intuitionistic fuzzy set (IFS) and the technique for order preference by similarity to an ideal solution (TOPSIS). The IFS is utilized to quantify opinions since it can effectively achieve a tradeoff between information retention and convenience of evaluation. Simultaneously, in the proposed UOD model, the weight updating mechanism is further considered to improve the interaction adequacy, and the unsupervised mechanism for interaction threshold helps to decrease the influences of subjectivity from DMs. Moreover, numerical simulations validate the UOD model’s feasibility. Finally, a school site selection problem is carried out to elaborate the effectiveness of the proposed method. This study will provide a methodological reference for solving large-scale MCGDM problems, facilitating rapid convergence of opinions within large-scale groups, and enrich the research on opinion dynamics in the field of decision-making.

Fluctuating wind load encountered by linearly moving vehicles: Investigation on fluctuating wind spectral characteristics

Wind load is one of the controlling loads for moving vehicles. Existing studies on the wind-induced response of vehicles mostly apply traditional wind spectra directly to the vehicles, overlooking their motion status. Fluctuating wind characteristics encountered by linearly moving vehicles were investigated in this paper. The traditional static frame of view was adjusted to a moving one, two principal flow directions were considered and the idea of positional equivalence introduced in Taylor frozen assumption was extended to the moving direction. A fluctuating wind power spectral density (PSD) was proposed. Then a novel test was designed to enable a measuring probe to travel across a wind field. The fluctuating wind characteristics encountered by moving points are quite different from those encountered by a static point. The integral scale decreases as the speed ratio of the measuring point to the wind increases, and an elliptic model was suggested. The proposed PSD aligns well with the test results, indicating that it can be used to accurately characterize the fluctuating wind load encountered by moving vehicles. Both the test results and the numerical analysis showed that as the speed ratio increases, the PSD and energy spectrum as a whole migrate to higher frequencies.

LDVI-SLAM: A Lightweight Monocular Visual-Inertial SLAM System for Dynamic Environments Based on Motion Constraints

Abstract Traditional Simultaneous Localization and Mapping (SLAM) systems are typically based on the assumption of a static environment. However, in practical applications, the presence of moving objects significantly reduces localization accuracy, limiting the system's versatility. To address the challenges of SLAM systems in dynamic environments, the academic community often employs computationally intensive methods such as deep learning, and some algorithms rely on expensive sensors (e.g., LiDAR or RGBD cameras) to obtain depth information. These factors increase computational complexity or hardware costs, complicating practical deployment. To improve localization accuracy and adaptability of SLAM systems in dynamic scenarios while maintaining low deployment costs, this paper proposes a dynamic environment robust monocular inertial SLAM system named LDVI-SLAM. The system uses more cost-effective sensors—monocular cameras and Inertial Measurement Unit (IMU)—along with lightweight computational methods. In LDVI-SLAM, first, the reliability of IMU data is verified. Then, using the ego-motion information provided by the IMU, along with epipolar constraint and an improved Rotation-aware Flow Vector Bound (R-FVB) constraint, dynamic feature points are eliminated. Additionally, this paper proposes a continuous tracking across interval frames method to enhance the distinction between static and dynamic feature points. Experimental results demonstrate that LDVI-SLAM performs effectively in dynamic environments and is easy to deploy. On the VIODE dataset, experimental results show that compared to the deep learning-based DynaSLAM, this method reduces the RMSE (Root Mean Square Error) of absolute trajectory error by 10.3%. Moreover, in terms of speed, under the same computing power, the single-frame processing speed of this method is comparable to ORB-SLAM3 and is two orders of magnitude faster than DynaSLAM, significantly outperforming deep learning-based SLAM algorithms. Experiments on the OMD dataset further prove that this method effectively avoids the risk of semantic classification errors, demonstrating better robustness and generality.

Sequential Point Clouds: A Survey.

Point clouds have garnered increasing research attention and found numerous practical applications. However, many of these applications, such as autonomous driving and robotic manipulation, rely on sequential point clouds, essentially adding a temporal dimension to the data (i.e., four dimensions) because the information of the static point cloud data could provide is still limited. Recent research efforts have been directed towards enhancing the understanding and utilization of sequential point clouds. This paper offers a comprehensive review of deep learning methods applied to sequential point cloud research, encompassing dynamic flow estimation, object detection & tracking, point cloud segmentation, and point cloud forecasting. This paper further summarizes and compares the quantitative results of the reviewed methods over the public benchmark datasets. Ultimately, the paper concludes by addressing the challenges in current sequential point cloud research and pointing towards promising avenues for future research.

Corrections to the Hamiltonian induced by finite-strength coupling to the environment.

If a quantum system interacts with the environment, then the Hamiltonian acquires a correction known as the Lamb-shift term. There are two other corrections to the Hamiltonian, related to the stationary state. Namely, the stationary state is to first approximation a Gibbs state with respect to original Hamiltonian. However, if we have finite coupling, then the true stationary state will be different, and regarding it as a Gibbs state to some effective Hamiltonian, one can extract a correction, which is called "steady-state" correction. Alternatively, one can take a static point of view, and consider the reduced state of total equilibrium state, i.e., system plus bath Gibbs state. The extracted Hamiltonian correction is called the "mean-force" correction. This paper presents several analytical results on second-order corrections (in coupling strength) of the three types mentioned above. Instead of the steady state, we focus on a state annihilated by the Liouvillian of the master equation, labeling it as the "quasi-steady state." Specifically, we derive a general formula for the mean-force correction as well as the quasi-steady state and Lamb-shift correction for a general class of master equations. Furthermore, specific formulas for corrections are obtained for the Davies, Bloch-Redfield, and cumulant equation(refined weak coupling). In particular, the cumulant equationserves as a case study of the Liouvillian, featuring a nontrivial fourth-order generator. This generator forms the basis for calculating the diagonal quasi-steady-state correction. We consider spin-boson model as an example, and in addition to using our formulas for corrections, we consider mean-force correction from reaction-coordinate approach.

Spatiotemporal vectorial structured light that dynamically varies on higher-order Poincaré sphere

Higher-order structured light beams, including optical vortex (OV) beams and vector beams, which can be geometrically represented as points on higher-order Poincaré spheres (HOPSs), have been widely exploited in applications such as optical trapping, optical communications, optical metrology, quantum optics, to name a few. To date, traditional approaches to producing such higher-order structured light beams deal with controllable generation of different static points on HOPS. In this paper, we propose and demonstrate the generation of spatiotemporal structured light beams that dynamically vary on HOPS. By superposing OV beams with different frequencies, spatiotemporal vectorial structured light beams that dynamically vary along latitude lines, meridians, and other trajectories on the first order Poincaré sphere are generated in simulation. Our work may give new insight into arbitrarily and ultrafast tailoring higher-order structured light beams.

BY-SLAM: Dynamic Visual SLAM System Based on BEBLID and Semantic Information Extraction.

SLAM is a critical technology for enabling autonomous navigation and positioning in unmanned vehicles. Traditional visual simultaneous localization and mapping algorithms are built upon the assumption of a static scene, overlooking the impact of dynamic targets within real-world environments. Interference from dynamic targets can significantly degrade the system's localization accuracy or even lead to tracking failure. To address these issues, we propose a dynamic visual SLAM system named BY-SLAM, which is based on BEBLID and semantic information extraction. Initially, the BEBLID descriptor is introduced to describe Oriented FAST feature points, enhancing both feature point matching accuracy and speed. Subsequently, FasterNet replaces the backbone network of YOLOv8s to expedite semantic information extraction. By using the results of DBSCAN clustering object detection, a more refined semantic mask is obtained. Finally, by leveraging the semantic mask and epipolar constraints, dynamic feature points are discerned and eliminated, allowing for the utilization of only static feature points for pose estimation and the construction of a dense 3D map that excludes dynamic targets. Experimental evaluations are conducted on both the TUM RGB-D dataset and real-world scenarios and demonstrate the effectiveness of the proposed algorithm at filtering out dynamic targets within the scenes. On average, the localization accuracy for the TUM RGB-D dataset improves by 95.53% compared to ORB-SLAM3. Comparative analyses against classical dynamic SLAM systems further corroborate the improvement in localization accuracy, map readability, and robustness achieved by BY-SLAM.

DOT-SLAM: A Stereo Visual Simultaneous Localization and Mapping (SLAM) System with Dynamic Object Tracking Based on Graph Optimization.

Most visual simultaneous localization and mapping (SLAM) systems are based on the assumption of a static environment in autonomous vehicles. However, when dynamic objects, particularly vehicles, occupy a large portion of the image, the localization accuracy of the system decreases significantly. To mitigate this challenge, this paper unveils DOT-SLAM, a novel stereo visual SLAM system that integrates dynamic object tracking through graph optimization. By integrating dynamic object pose estimation into the SLAM system, the system can effectively utilize both foreground and background points for ego vehicle localization and obtain a static feature points map. To rectify the inaccuracies in depth estimation from stereo disparity directly on the foreground points of dynamic objects due to their self-similarity characteristics, a coarse-to-fine depth estimation method based on camera-road plane geometry is presented. This method uses rough depth to guide fine stereo matching, thereby obtaining the 3 dimensions (3D)spatial positions of feature points on dynamic objects. Subsequently, by establishing constraints on the dynamic object's pose using the road plane and non-holonomic constraints (NHCs) of the vehicle, reducing the initial pose uncertainty of dynamic objects leads to more accurate dynamic object initialization. Finally, by considering foreground points, background points, the local road plane, the ego vehicle pose, and dynamic object poses as optimization nodes, through the establishment and joint optimization of a nonlinear model based on graph optimization, accurate six degrees of freedom (DoFs) pose estimations are obtained for both the ego vehicle and dynamic objects. Experimental validation on the KITTI-360 dataset demonstrates that DOT-SLAM effectively utilizes features from the background and dynamic objects in the environment, resulting in more accurate vehicle trajectory estimation and a static environment map. Results obtained from a real-world dataset test reinforce the effectiveness.

OMS-SLAM: dynamic scene visual SLAM based on object detection with multiple geometric feature constraints and statistical threshold segmentation

Abstract SLAM technology is crucial to robot navigation. Despite the good performance of traditional SLAM algorithms in static environments, dynamic objects typically exist in realistic operating environments. These objects can lead to misassociated features, which in turn considerably impact the system’s localization accuracy and robustness. To better address this challenge, we have proposed the OMS-SLAM. In OMS-SLAM, we adopted the YOLOv8 target detection network to extract object information from environment and designed a dynamic probability propagation model that is coupled with target detection and multiple geometric constrains to determine the dynamic objects in the environment. For the identified dynamic objects, we have designed a foreground image segmentation algorithm based on depth image histogram statistics to extract the object contours and eliminate the feature points within these contours. We then use the GMS (Grid-based Motion Statistics) matching pair as the filtering strategy to enhance the quality of the feature points and use the enhanced feature points for tracking. This combined method can accurately identify dynamic objects and extract related feature points, significantly reducing its interference and consequently enhancing the system's robustness and localization accuracy. We also built static dense point cloud maps to support advanced tasks of robots. Finally, through testing on the high-speed dataset of TUM RGB-D, it was found that the root mean square error of the Absolute Trajectory Error (ATE) in this study decreased by an average of 97.10%, compared to ORB-SLAM2. Moreover, tests in real-world scenarios also confirmed the effectiveness of the OMS-SLAM algorithm in dynamic environments.

Alternate computation of gravitational effects from a single loop of inflationary scalars

We present a new computation of the renormalized graviton self-energy induced by a loop of massless, minimally coupled scalars on de Sitter background. Our result takes account of the need to include a finite renormalization of the cosmological constant, which was not included in the first analysis. We also avoid preconceptions concerning structure functions and instead express the result as a linear combination of 21 tensor differential operators. By using our result to quantum-correct the linearized effective field equation we derive logarithmic corrections to both the electric components of the Weyl tensor for gravitational radiation and to the two potentials which quantify the gravitational response to a static point mass.