Field Of Navigation Research Articles

Dynamic Calibration and Multi-View Fusion of Active Vision Tracking System for Surgical Instrument

Abstract In recent years, surgical instrument tracking has gained significant attention in the field of surgical navigation. An often-overlooked problem lies in the instrument’s irregular movement conflicting with the tracking system’s fixed viewpoint, leading to line-of-sight (LOS) occlusion, marker occlusion, and sub-optimal accuracy. The active vision tracking system (which actively adjusts the camera viewpoint) is applied to address this issue. In this paper, two algorithms of multi-view fusion and dynamic calibration are proposed in multi-camera active vision to allow each camera to adjust its viewpoint independently. These algorithms are integrated into the parallel computing of the frontend and backend to implement the positioning of the instrument from multiple perspectives and real-time calibration of multiple cameras. Simulations and experiments are carried out to test the accuracy and robustness of the proposed tracking system. Results reveal that our system achieves a closer trajectory (0.12 mm) to the ground truth compared to a stereo camera tracking system (0.32 mm). The proposed tracking system is able to keep track of the instruments within the positioning volume at different surgical phases, ensuring consistent navigation and improving positioning stability and accuracy.

Migratory birds can extract positional information from magnetic inclination and magnetic declination alone

Migratory birds are able to navigate over great distances with remarkable accuracy. The mechanism they use to achieve this feat is thought to involve two distinct steps: locating their position (the ‘map’) and heading towards the direction determined (the ‘compass’). For decades, this map-and-compass concept has shaped our perception of navigation in animals, although the nature of the map remains debated. However, some recent studies suggest the involvement of the Earth’s magnetic field in the map step. Here, we tested whether migratory songbirds, Eurasian reed warblers ( Acrocephalus scirpaceus ), can determine their position based on two magnetic field components that are also associated with direction finding, i.e. magnetic inclination and magnetic declination. During a virtual magnetic displacement experiment, the birds were exposed to altered magnetic inclination and magnetic declination values that would indicate a displacement from their natural migratory corridor, but the total intensity of the field remained unchanged, creating a spatial mismatch between these components. The response was a change in the birds’ migratory direction consistent with a compensatory re-orientation. This suggests that birds can extract positional as well as directional information from these cues, even when they are in conflict with another component of the magnetic field. It remains to be seen whether birds use the total intensity of Earth’s magnetic field for navigation.

Repulsion of CA3 / dentate gyrus representations is driven by distinct internal beliefs in the face of ambiguous sensory input.

Recent human neuroimaging studies of episodic memory have revealed a counterintuitive phenomenon in the hippocampus: when events are highly similar, corresponding hippocampal activity patterns are sometimes less correlated than activity patterns associated with unrelated events. This phenomenon- repulsion- is not accounted for by most theories of the hippocampus, and the conditions that trigger repulsion remain poorly understood. Here, we used a spatial route-learning task and high-resolution fMRI in humans to test whether hippocampal repulsion is fundamentally driven by internal beliefs about the environment. By precisely measuring participants' internal beliefs and actively manipulating them, we show that repulsion selectively occurred in hippocampal subfields CA3 and dentate gyrus when visual input was ambiguous-or even identical -but internal beliefs were distinct. These findings firmly establish conditions that elicit repulsion and have broad relevance to theories of hippocampal function and to the fields of human episodic memory and rodent spatial navigation.

Target-specified reference-based deep learning network for joint image deblurring and resolution enhancement in surgical zoom lens camera calibration

Background and objectiveFor the augmented reality of surgical navigation, which overlays a 3D model of the surgical target on an image, accurate camera calibration is imperative. However, when the checkerboard images for calibration are captured using a surgical microscope having high magnification, blur owing to the narrow depth of focus and blocking artifacts caused by limited resolution around the fine edges occur. These artifacts strongly affect the localization of corner points of the checkerboard in these images, resulting in inaccurate calibration, which leads to a large displacement in augmented reality. To solve this problem, in this study, we proposed a novel target-specific deep learning network that simultaneously enhances both the blur and spatial resolution of an image for surgical zoom lens camera calibration. MethodsAs a scheme of an end-to-end convolutional deep neural network, the proposed network is specifically intended for the checkerboard image enhancement used in camera calibration. Through the symmetric architecture of the network, which consists of encoding and decoding layers, the distinctive spatial features of the encoding layers are transferred and merged with the output of the decoding layers. Additionally, by integrating a multi-frame framework including subpixel motion estimation and ideal reference image with the symmetric architecture, joint image deblurring and enhanced resolution were efficiently achieved. ResultsFrom experimental comparisons, we verified the capability of the proposed method to improve the subjective and objective performances of surgical microscope calibration. Furthermore, we confirmed that the augmented reality overlap ratio, which quantitatively indicates augmented reality accuracy, from calibration with the enhanced image of the proposed method is higher than that of the previous methods. ConclusionsThese findings suggest that the proposed network provides sharp high-resolution images from blurry low-resolution inputs. Furthermore, we demonstrate superior performance in camera calibration by using surgical microscopic images, thus showing its potential applications in the field of practical surgical navigation.

3D Voxel-based Path Planning for AVs in Dynamic Complex Environments

Abstract. With the rapid advancement of science and technology, autonomous driving technology has been widely applied in various fields such as the automotive industry, intelligent warehousing, and emergency rescue. Traditional 2D path planning has limitations in dealing with the complex and dynamic environments of the real world, making it difficult to meet the high precision and complex path planning requirements of autonomous vehicles. With the development of data acquisition technology, large-scale point cloud data has become easily accessible, which can better extract the three-dimensional complex of the environment. Nevertheless, the unstructured nature and vast amount of point cloud data make path planning on it extremely challenging. Voxel models can effectively compress point cloud data and provide neighboring topological structures. In view of this, we propose a voxel-based framework for the path planning of autonomous vehicles in dynamic and complex 3D environments, which involves converting point cloud data into voxels and extracting navigation spaces. Subsequently, deep reinforcement learning techniques are utilized to achieve obstacle avoidance and navigation on the voxel map. It is expected that this framework will contribute to the development of 3D path planning and enhance the utilization of point cloud data in the navigation field.

Enhancing Exosomal Delivery to Abdominal Aortic Aneurysms using Magnetically Responsive Chemotactic Nanomotors for Elastic Matrix Regenerative Repair.

Abdominal aortic aneurysms (AAAs) involve localized dilation of the abdominal aorta, with the reversal of this condition being significantly limited by the inherently poor and abnormal regenerative repair of the aortic elastic matrix. Mesenchymal stem cell exosomes (MSCEs) are promising regenerative tools; however, achieving precise targeting of AAA with MSCEs is challenging owing to the high blood flow in the arterial system. In this study, an engineered exosomal nanomotor is developed for magnetic and chemical propulsion. The results demonstrate that this nanomotor effectively enhances the delivery of MSCEs to the AAA through magnetic field navigation and catalase-induced chemotaxis. The nanomotor significantly enhances the elastic matrix repair, reduces oxidative stress, and activates the PI3K/Akt pathway, leading to aneurysm shrinkage and reversal. In addition, the nanomotor possesses magnetic resonance imaging capabilities. The use of this nanomotor offers a novel, targeted drug delivery system in a rat model of AAA and holds promise as a potential therapeutic option for this condition.

An Efficient Attentive-GRU Structure for Uncertainty Modelling of Crowdsourced Human Trajectories Under Building-obscured Urban Scenes

Abstract. Uncertainty modelling is regarded as one of the core components in the field of human mobility analysis and urban navigation, that can affect the performance of human behaviour modelling and location information acquisition. Existing uncertainty modelling algorithms towards the human movement trajectory are subjected to random and highly dynamic human motion characteristics and sampling and observation errors of Global Navigation Satellite System (GNSS) signals caused by the occlusion of buildings in urban scenes, which lead to the insufficient spatiotemporal correlation and poor accuracy of uncertainty modelling. To fill in this gap, this paper proposes an efficient attentive-GRU structure for uncertainty modelling of crowdsourced human trajectories under building-obscured urban scenes, that takes into account both temporal correlation and spatial correlation of human-originated GNSS trajectories and related motion features. A period of human motion data is modelled instead of only one or adjacent location points to avoid interference factors caused by the obstruction of urban buildings, and time-varying measurement and sampling errors are further estimated and combined with comprehensive human motion features to improve the accuracy of final uncertainty modelling. Comprehensive experiments indicate that compared with existing uncertainty modelling methods including physical models and deep-learning models, the proposed attentive-GRU structure realizes much better performance under different accuracy indexes.

An Enhanced Seamless Localization Framework Using Spatial-temporal Uncertainty Predictor Under Obscured Indoor and Outdoor Scenes

Abstract. Uncertainty modelling is regarded as one of the core components in the field of urban navigation, that can affect the performance of indoor and outdoor location information acquisition, especially under obscured scenarios. Existing multi-source fusion-based seamless positioning algorithms are subjected to random and highly dynamic human motion characteristics and changeable observation errors caused by the dynamic occlusions of human and buildings under urban scenes, which lead to the insufficient spatiotemporal correlation and poor accuracy of final multi-source fusion structure. To fill in this gap, this paper proposes an enhanced seamless localization framework using spatial-temporal uncertainty predictor under obscured indoor and outdoor scenes (ESL-STUP), that takes into account both temporal correlation and spatial correlation of trajectories provided by Wi-Fi, GNSS, and sensor-originated motion information. An iPDR-based trajectory estimation structure is proposed, using the integration of INS/PDR mechanizations, magnetic observations, and deep-learning based speed estimation to enhance the performance of traditional PDR algorithm. A period of human motion features extracted from hybrid location sources are modelled instead of only one or adjacent location points to realize time-varying measured uncertainty errors prediction, and the predicted uncertainty errors of different indoor and outdoor location sources are integrated with iPDR to realize robust seamless positioning performance. Comprehensive experiments indicate that compared with existing multi-source fusion-based seamless positioning structure, the proposed ESL-STUP realizes much better performance under different scenes.

Performance Evaluation of a Bioinspired Geomagnetic Sensor and Its Application for Geomagnetic Navigation in Simulated Environment.

For geomagnetic navigation technology, taking inspiration from nature and leveraging the principle of animals' utilization of the geomagnetic field for long-distance navigation, and employing biomimetic technology to develop higher-precision geomagnetic sensors and more advanced navigation strategies, has emerged as a new trend. Based on the two widely acknowledged biological magnetic induction mechanisms, we have designed a bioinspired weak magnetic vector (BWMV) sensor and integrated it with neural networks to achieve geomagnetic matching navigation. In this paper, we assess the performance of the BWMV sensor through finite element model simulation. The result validates its high measurement accuracy and outstanding adaptability to installation errors with the assistance of specially trained neural networks. Furthermore, we have enhanced the bioinspired geomagnetic navigation algorithm and proposed a more advanced search strategy to adapt to navigation under the condition of no prior geomagnetic map. A simulated geomagnetic navigation platform was constructed based on the finite element model to simulate the navigation of the BWMV sensor in geomagnetic environments. The simulated navigation experiment verified that the proposed search strategy applied to the BWMV sensor can achieve high-precision navigation. This study proposes a novel approach for the research of bioinspired geomagnetic navigation technology, which holds great development prospects.

Analysis of multipath effects on Low Earth Orbit navigation satellites

Abstract With the large-scale emergence of Low-Earth Orbit (LEO) internet constellations, the direct or indirect utilization of LEO communication constellations for navigation and positioning has become a hot topic of current research. However, the multipath effect remains a critical factor influencing positioning accuracy. Compared to traditional Medium-Earth Orbit (MEO) satellites, LEO satellites move swiftly, resulting in faster multipath variations, which renders traditional multipath envelope analysis methods inapplicable. This study proposes a novel analytical model tailored to the multipath effect characteristics of LEO satellites. Through this model, we have observed that the multipath variations of high-frequency signals in LEO rapidly fluctuate near zero, offering the potential to reduce errors through smoothing methods. Furthermore, the study reveals that the impact of the multipath effect is positively correlated with orbital altitude and inversely correlated with carrier frequency. This study provides a new perspective for understanding the multipath effect characteristics of LEO satellites and offers necessary theoretical support and practical guidance for improving the accuracy of navigation and positioning systems based on LEO satellites. Future research can further explore multipath suppression methods and promote the widespread application of LEO satellites in the field of navigation and positioning.

Study on the suppression of secondary electron emission characteristics based on TiN

Abstract With the continuous development of satellite communications, the problem of high-frequency high-power multipactor caused by the miniaturization of microwave passive components in the aerospace field has become more and more serious. In this paper, for the problem of multipactor suppression of load passive devices, we carried out research on the preparation of TiN thin films based on PEALD and put forward a new method for suppressing SEE with good suppression effect, high consistency, low surface resistance, and other aspects of the unity of the multipactor effect. The use of 20nm TiN coating can make the silver-plated surface secondary electron emission coefficient down to 1.59. The RF surface resistance change rate is less than 3%. In the field of communication, satellite and navigation and other fields have a wide range of application prospects.

Robust Indoor Positioning with Smartphone by Utilizing Encoded Chirp Acoustic Signal.

Recently, indoor positioning has been one of the hot topics in the field of navigation and positioning. Among different solutions on indoor positioning, positioning with acoustic signals has its promise due to its relatively high accuracy in the line of sight scenarios, low cost, and ease of being implemented in smartphones. In this work, a novel acoustic positioning method, called RATBILS, is proposed, in which encoded chirp acoustic signals are modulated and transmitted by different acoustic base stations. The smartphones receive the signals and perform the following three steps: (1) preprocessing; (2) time of arrival (TOA) estimation; and (3) time difference of arrival (TDOA) calculation and location estimation. In the preprocessing stage, we use band pass filters to filter out low-frequency noise from the environment. At the same time, we perform a signal decoding function in order to lock onto the positioning source. In the TOA estimation stage, we conduct both coarse and fine detection to enhance the accuracy and robustness of TOA estimation. The primary goal of coarse detection is to establish a noise range for fine detection. The main objective of fine detection is to emphasize the intensity of the first arrival diameter and resistance with multipath and non-line-of-sight (NLOS) caused by human body obstruction. In the TDOA calculation and location estimation stage, we estimate the TDOA based on the TOA estimation and then use the TDOA results for position estimation. In order to evaluate the performance of the proposed RATBILS system, two indoor field tests are carried out. The test results show that the RATBILS system achieves a positioning error of 0.23 m at 92% in region 1 of scene 1 and is superior to the traditional threshold method. The RATBILS system achieves a positioning error of 0.56 m at 92% in region 2 of scene 1 and is superior to the traditional threshold method. In scene 2, the maximum average positioning error was 1.26 m, which is better than the 3.33 m and 3.87 m of the two traditional threshold methods.

Development of Advanced Aluminum and Magnesium Alloys: Microstructure, Mechanical Properties and Processing

Mg and Al alloys are the first and second engineering light metals, which are widely used in the aviation, aerospace, navigation, automotive, and electronic fields [...]

A Novel FECAM-iTransformer Algorithm for Assisting INS/GNSS Navigation System during GNSS Outages

In the field of navigation and positioning, the inertial navigation system (INS)/global navigation satellite system (GNSS) integrated navigation system is known for providing stable and high-precision navigation services for vehicles. However, in extreme scenarios where GNSS navigation data are completely interrupted, the positioning accuracy of these integrated systems declines sharply. While there has been considerable research into using neural networks to replace the GNSS signal output during such interruptions, these approaches often lack targeted modeling of sensor information, resulting in poor navigation stability. In this study, we propose an integrated navigation system assisted by a novel neural network: an inverted-Transformer (iTransformer) and the application of a frequency-enhanced channel attention mechanism (FECAM) to enhance its performance, called an INS/FECAM-iTransformer integrated navigation system. The key advantage of this system lies in its ability to simultaneously extract features from both the time and frequency domains and capture the variable correlations among multi-channel measurements, thereby enhancing the modeling capabilities for sensor data. In the experimental part, a public dataset and a private dataset are used for testing. The best experimental results show that compared to a pure INS inertial navigation system, the position error of the INS/FECAM-iTransformer integrated navigation system reduces by up to 99.9%. Compared to the INS/LSTM (long short-term memory) and INS/GRU (gated recurrent unit) integrated navigation systems, the position error of the proposed method decreases by up to 82.4% and 78.2%, respectively. The proposed approach offers significantly higher navigation accuracy and stability.

A New Data Fusion Method for GNSS/INS Integration Based on Weighted Multiple Criteria

The standard Kalman filter and most of its enhancements are typically designed based on the criterion that minimizes the mean squared error, with little discussion of multiple criteria in the positioning and navigation fields. Therefore, a novel data fusion method that takes into account weighted multiple criteria is proposed in this paper, implementing a filtering algorithm based on integrated criteria with different weights determined by a weight adjustment factor. The proposed algorithm and conventional filtering algorithms were utilized for data fusion in GNSS/INS integration. Experiments were conducted using actual data collected from an urban environment. Comparative analysis revealed that, when utilizing the proposed algorithm, the precision of the position, velocity, and attitude of the tested land vehicle could be improved by approximately 24%, 48%, and 35%, respectively. Furthermore, a series of filtering algorithms with different weight adjustment factors was performed to test their influence on the filtering. The application of the proposed algorithm should be accompanied by an appropriate weight adjustment factor.

An accurate smartphone-based indoor pedestrian localization system using ORB-SLAM camera and PDR inertial sensors fusion approach

Using one’s own smartphone for indoor navigation, especially for visually impaired individuals, can be a valuable and empowering tool to promote independence and safe mobility. Additionally, the integration of Pedestrian Dead Reckoning (PDR), which relies on smartphone sensors like accelerometers and gyroscopes, is a well-known and widely used approach in the field of indoor navigation. However, despite continuous improvements in sensor technology, addressing drift issues remains a principal problem in the effective implementation of PDR systems. On the other hand, although the use of Simultaneous Localization and Mapping (SLAM) technologies in smartphones for indoor navigation has also gained significant adoption in recent years, there are indeed challenges associated with SLAM, including low localization accuracy and potential tracking divergence. To overcome the above limitations, this paper presents a novel and promising solution for smartphone-based indoor localization, addressing the limitations of both PDR and SLAM through their synergistic integration. In fact, to correct the accumulated errors of the proposed localization system and thus improve the accuracy, our approach uses an ORB-SLAM camera and PDR inertial sensors via a Kalman filter to achieve back-end fusion. Experimental results show that our localization system significantly reduces the mean localization error by 62%, marking a considerable improvement over the standalone PDR and SLAM systems. Moreover, compared with state-of-the-art techniques, our system achieves high localization accuracy, with an improvement of 59%, making it highly suitable for today’s real-time applications, especially beneficial for visually impaired people.

Fractional Strong Metric Dimension of Convex Polytopes and its applications

The fractional versions of various metric related parameters have recently gained importance due to their applications in the fields of sensor networking, robot navigation and linear optimization problems. Convex polytopes are collection of those polytopes of Euclidean space which are their convex subsets. They have key importance in the field of network designing due to their stable and resilient structure which aids optimal data transfer. The identification and removal of components (nodes) of a communication network causing abruption in its flow is of key importance for optimal data transmission. These components are referred as strong resolving neighbourhood (SRNs) in graph theory and assigning least weight to these components aids the computation of fractional strong metric dimension (FSMD). In this paper, we compute FSMD for certain convex polytopes which include $\mathbb{P}_{n}$, $\mathbb{P}_{n}^{1}$ and $\mathbb{P}_{n}^{2}$. In this regard, it is shown that for $n \geq 3$, FSMD of $\mathbb{P}_{n}$ and $\mathbb{P}_{n}^{2}$ is $n$ and $\frac{3n}{2}$, respectively. Also, FSMD of $\mathbb{P}_{n}^{1}$ is $n$ when $n$ is odd and $\frac{3n}{2}$ when $n$ is even. Finally, an application of FSMD in the context of internet connection networks is furnished.

Plug-and-Play Cooperative Navigation: From Single-Agent Navigation Fields to Graph- Maintaining Distributed MAS Controllers

Plug-and-Play Cooperative Navigation: From Single-Agent Navigation Fields to Graph- Maintaining Distributed MAS Controllers

Recording of single-unit activities with flexible micro-electrocorticographic array in rats for decoding of whole-body navigation

Objective.Micro-electrocorticographic (μECoG) arrays are able to record neural activities from the cortical surface, without the need to penetrate the brain parenchyma. Owing in part to small electrode sizes, previous studies have demonstrated that single-unit spikes could be detected from the cortical surface, and likely from Layer I neurons of the neocortex. Here we tested the ability to useμECoG arrays to decode, in rats, body position during open field navigation, through isolated single-unit activities.Approach. μECoG arrays were chronically implanted onto primary motor cortex (M1) of Wistar rats, and neural recording was performed in awake, behaving rats in an open-field enclosure. The signals were band-pass filtered between 300-3000 Hz. Threshold-crossing spikes were identified and sorted into distinct units based on defined criteria including waveform morphology and refractory period. Body positions were derived from video recordings. We used gradient-boosting machine to predict body position based on previous 100 ms of spike data, and correlation analyses to elucidate the relationship between position and spike patterns.Main results.Single-unit spikes could be extracted during chronic recording fromμECoG, and spatial position could be decoded from these spikes with a mean absolute error of prediction of 0.135 and 0.090 in the x- and y- dimensions (of a normalized range from 0 to 1), and Pearson's r of 0.607 and 0.571, respectively.Significance. μECoG can detect single-unit activities that likely arise from superficial neurons in the cortex and is a promising alternative to intracortical arrays, with the added benefit of scalability to cover large cortical surface with minimal incremental risks. More studies should be performed in human related to its use as brain-machine interface.

VE-LIOM: A Versatile and Efficient LiDAR-Inertial Odometry and Mapping System

LiDAR has emerged as one of the most pivotal sensors in the field of navigation, owing to its expansive measurement range, high resolution, and adeptness in capturing intricate scene details. This significance is particularly pronounced in challenging navigation scenarios where GNSS signals encounter interference, such as within urban canyons and indoor environments. However, the copious volume of point cloud data poses a challenge, rendering traditional iterative closest point (ICP) methods inadequate in meeting real-time odometry requirements. Consequently, many algorithms have turned to feature extraction approaches. Nonetheless, with the advent of diverse scanning mode LiDARs, there arises a necessity to devise unique methods tailored to these sensors to facilitate algorithm migration. To address this challenge, we propose a weighted point-to-plane matching strategy that focuses on local details without relying on feature extraction. This improved approach mitigates the impact of imperfect plane fitting on localization accuracy. Moreover, we present a classification optimization method based on the normal vectors of planes to further refine algorithmic efficiency. Finally, we devise a tightly coupled LiDAR-inertial odometry system founded upon optimization schemes. Notably, we pioneer the derivation of an online gravity estimation method from the perspective of S2 manifold optimization, effectively minimizing the influence of gravity estimation errors introduced during the initialization phase on localization accuracy. The efficacy of the proposed method was validated through experimentation employing various LiDAR sensors. The outcomes of indoor and outdoor experiments substantiate its capability to furnish real-time and precise localization and mapping results.