Simultaneous Localization And Mapping Framework Research Articles

A new covariance intersection based integrated SLAM framework for 3D outdoor agricultural applications

AbstractThis letter introduces a novel integrated framework for simultaneous localization and mapping (SLAM) tailored for general agricultural applications. The framework combines a cutting‐edge SLAM method, LIO‐SAM, with covariance intersection for sensor fusion. Agricultural robots often operate in unstructured environments with sparse feature points and encounter repeated similar information, such as trees. Therefore, a fusion framework based on 3D SLAM augmented with additional information, such as feature‐independent GPS data, becomes essential. This study proposes an integrated SLAM framework by introducing a convergence strategy based on covariance analysis, incorporating a state‐of‐the‐art 3D SLAM technique. The convergence methods, namely “dynamic weight assignment” and “winner takes all”, are presented alternatively, tailored to seamlessly integrate with the proposed framework. Evaluations using a public dataset and an experiment demonstrate the effectiveness of the approach through numerical analysis and visual representation. The results illustrate that this method surpasses conventional approaches in accurately estimating the robot's position. In the future, this research will focus on automating crop cultivation and harvesting by integrating the proposed system with robot arm control.

Lp-slam: language-perceptive RGB-D SLAM framework exploiting large language model

AbstractWith the development of deep learning, a higher level of perception of the environment such as the semantic level can be achieved in the simultaneous localization and mapping (SLAM) domain. However, previous works did not achieve a natural-language level of perception. Therefore, LP-SLAM (Language-Perceptive RGB-D SLAM) is proposed that leverages large language models (LLMs). The texts in the scene can be detected by scene text recognition (STR) and mapped as landmarks with a task-driven selection. A text error correction chain (TECC) is designed with a similarity classification method, a two-stage memory strategy, and a text clustering method. The proposed architecture is designed to deal with the mis-detection and mis-recognition cases of STR and to provide accurate text information to the framework. The proposed framework takes input images and generates a 3D map with sparse point cloud and task-related texts. Finally, a natural user interface (NUI) is designed based on the constructed map and LLM, which gives position instructions based on users’ natural queries. The experimental results validated the proposed TECC design and the overall framework. We publish the virtual dataset with ground truth, as well as the source code for further research. https://github.com/GroupOfLPSLAM/LP_SLAM.

LGD: A fast place recognition method based on the fusion of local and global descriptors

Place recognition, which is also known as loop detection, has been one of the main problems in the field of robot navigation, and it is crucial in realizing high-performance simultaneous localization and mapping (SLAM). Most of the existing methods directly construct local or global descriptors with point clouds, ignoring the offset of different visits in the same place. To overcome the current limitations, this study emphasizes the importance of point cloud features in improving the environment recognition precision and proposes an innovative framework that combines local features that are spatially invariant and prominent descriptive global features as a representation of a laser scanner frame. The feature center of the laser point cloud is obtained by computing local feature points with spatial invariance, which improves the offset between the original point cloud and the feature center. Furthermore, a coarse-to-fine hierarchical recognition strategy is developed to improve the efficiency of place recognition. An evaluation with different offsets and synthesis is conducted on the publicly available KITTI and self-developed CHDloop datasets. The experimental results show that the proposed method can improve time efficiency by more than 18% compared to the SC and ISC, and there are also significant improvements in the recall rate and accuracy in slight lane-change scenarios. Moreover, the proposed method can obtain rich lane-change loops in moderate and large lane-change scenarios where the SC and ISC fail to obtain effective loops. The proposed method can be used as a lightweight and interpretable approach for place recognition, which not only can be quickly deployed in any SLAM framework but can also efficiently handle more complex place recognition issues in urban environments.

A Review of Dynamic Object Filtering in SLAM Based on 3D LiDAR.

SLAM (Simultaneous Localization and Mapping) based on 3D LiDAR (Laser Detection and Ranging) is an expanding field of research with numerous applications in the areas of autonomous driving, mobile robotics, and UAVs (Unmanned Aerial Vehicles). However, in most real-world scenarios, dynamic objects can negatively impact the accuracy and robustness of SLAM. In recent years, the challenge of achieving optimal SLAM performance in dynamic environments has led to the emergence of various research efforts, but there has been relatively little relevant review. This work delves into the development process and current state of SLAM based on 3D LiDAR in dynamic environments. After analyzing the necessity and importance of filtering dynamic objects in SLAM, this paper is developed from two dimensions. At the solution-oriented level, mainstream methods of filtering dynamic targets in 3D point cloud are introduced in detail, such as the ray-tracing-based approach, the visibility-based approach, the segmentation-based approach, and others. Then, at the problem-oriented level, this paper classifies dynamic objects and summarizes the corresponding processing strategies for different categories in the SLAM framework, such as online real-time filtering, post-processing after the mapping, and Long-term SLAM. Finally, the development trends and research directions of dynamic object filtering in SLAM based on 3D LiDAR are discussed and predicted.

DLOAM: Real-time and Robust LiDAR SLAM System Based on CNN in Dynamic Urban Environments

Dynamic object detection, state estimation, and map-building are crucial for autonomous robot systems and intelligent transportation applications in urban scenarios. Most current LiDAR Simultaneous Localization and Mapping (SLAM) systems operate on the assumption that the observed environment is static. However, the overall accuracy and robustness of a SLAM system can be compromised by dynamic objects in the environment. Aiming at the problem of inaccurate odometry estimation and wrong mapping caused by the existing LiDAR SLAM method which cannot detect the dynamic objects, we study the SLAM problem of robots and unmanned vehicles equipped with LiDAR traveling in the dynamic urban scenes. We propose a fast LiDAR-only model-free dynamic objects detection method, which uses the spatial and temporal information of point cloud through a convolutional neural network (CNN), and the detection accuracy is improved by 35 use spatial information. We further integrate it into a state-of-the-art LiDAR SLAM framework to improve the SLAM performance. Firstly, the range image constructed by LiDAR point cloud is used for ground extraction and non-ground point clustering. Then, the motion of objects in the scene is estimated by the difference between adjacent frames, and the segmented objects are further divided into dynamic objects and static objects by their motion features. After that, the stable feature points are extracted from the static objects. Finally, the pose transformation of adjacent frames is solved by matching feature point pairs. We evaluated the accuracy and robustness of our system on datasets with different challenging dynamic environments, and the results show our system has significant improvements in accuracy and robustness of odometry and mapping, while still maintain real-time performance, which is sufficient for autonomous robot systems and intelligent transportation applications in urban scenarios.

A multi-sensor fusion framework with tight coupling for precise positioning and optimization

In the dynamic landscape of artificial intelligence and robotics, the pursuit of accurate positioning in mobile robots has intensified. This research addresses the limitations of single-sensor SLAM (Simultaneous Localization and Mapping) techniques in complex settings by harnessing the collective strengths of LiDAR (Light Detection And Ranging), Camera, IMU (Inertial Measurement Unit), and GNSS (Global Navigation Satellite System) sensors. The proposed multi-sensor tightly-coupled SLAM framework is an integration of point-line feature-based laser–visual–inertial odometry, visual–laser fusion loop closure detection, and factor graph-based back-end optimization. Within the visual–inertial subsystem, an advanced LSD (Line Segment Detector) feature extraction strategy is introduced, incorporating point-line fusion to enhance visual line features. Additionally, the laser point cloud is projected onto the camera coordinate system, establishing depth associations with visual attributes. Strengthening the robustness of the visual–inertial subsystem in low-texture environments, camera poses undergo optimization through a sliding-window bundle adjustment method. In the laser-inertial subsystem, IMU preintegration mitigates laser point cloud distortion. Extracting edge and plane features, coupled with frame-to-local-map matching, enhances matching efficiency while streamlining computational intricacies. This amalgamation forms the basis of the laser–visual–inertial odometry fusion system. To overcome the limitations of standalone visual and laser-based loop closure detection, a dual-loop closure method utilizing visual–laser fusion is proposed. Leveraging the DBoW2 bag-of-words model, complemented by temporal–spatial consistency checks, enhances detection efficiency and accuracy. The integration of GNSS factors imparts global constraints for expansive outdoor scenarios. Employing factor graph-based back-end optimization, the refinement of laser–visual–inertial odometry factors, visual–inertial odometry factors, IMU preintegration factors, loop closure factors, and GNSS factors culminates in precise global pose estimation and high-fidelity point cloud maps. Through rigorous evaluation of the M2DGR dataset and a mobile robot platform, the proposed methodology emerges as an exemplar of performance, showcasing superiority over the state-of-the-art LIO-SAM technique. Achieving a reduction of 2.86m and 3.23m in the root mean square error of absolute pose estimation across divergent environments, this approach exhibits remarkable efficacy in outdoor scenarios, thereby elevating the precision and resilience of SLAM algorithms for mobile robots.

A fully‐automatic side‐scan sonar simultaneous localization and mapping framework

AbstractSide‐scan sonar is a lightweight acoustic sensor that is frequently deployed on autonomous underwater vehicles (AUVs) to provide high‐resolution seafloor images. However, using side‐scan images to perform simultaneous localization and mapping (SLAM) remains a challenge when there is a lack of 3D bathymetric information and discriminant features in the side‐scan images. To tackle this, the authors propose a feature‐based SLAM framework using side‐scan sonar, which is able to automatically detect and robustly match keypoints between paired side‐scan images. The authors then use the detected correspondences as constraints to optimise the AUV pose trajectory. The proposed method is evaluated on real data collected by a Hugin AUV, using as a ground truth reference both manually‐annotated keypoints and a 3D bathymetry mesh from multibeam echosounder (MBES). Experimental results demonstrate that this approach is able to reduce drifts from the dead‐reckoning system. The framework is made publicly available for the benefit of the community.

A Semantic Topology Graph to Detect Re-Localization and Loop Closure of the Visual Simultaneous Localization and Mapping System in a Dynamic Environment.

Simultaneous localization and mapping (SLAM) plays a crucial role in the field of intelligent mobile robots. However, the traditional Visual SLAM (VSLAM) framework is based on strong assumptions about static environments, which are not applicable to dynamic real-world environments. The correctness of re-localization and recall of loop closure detection are both lower when the mobile robot loses frames in a dynamic environment. Thus, in this paper, the re-localization and loop closure detection method with a semantic topology graph based on ORB-SLAM2 is proposed. First, we use YOLOv5 for object detection and label the recognized dynamic and static objects. Secondly, the topology graph is constructed using the position information of static objects in space. Then, we propose a weight expression for the topology graph to calculate the similarity of topology in different keyframes. Finally, the re-localization and loop closure detection are determined based on the value of topology similarity. Experiments on public datasets show that the semantic topology graph is effective in improving the correct rate of re-localization and the accuracy of loop closure detection in a dynamic environment.

A Collaborative Graph-based SLAM Framework Using a Computationally Effective Measurement Algebra

Simultaneous localization and mapping (SLAM) is an essential task for autonomous rover navigation in an unknown environment, especially if no absolute location information is available. This paper presents a computationally lightweight framework to enable agents with limited processing power to carry out the SLAM cooperatively and without absolute onboard localization sensors in a 2D environment. The proposed solution is built on a graph-based map representation, where nodes (resp. edges) represent landmarks (resp. odometry-based relative measurements), a measurement algebra with embedded uncertainty, and a compact database format that could be stored on a server in a centralized manner. The operations required by the agents to insert a new landmark in the graph, update landmark positions and combine measurements as a loop is closed in the graph are detailed. The resulting framework was tested in a laboratory environment and on a public dataset with encouraging results; hence our method can be used for cost-effective indoor mobile agents with limited computational resources and onboard sensors to achieve a mapping while keeping track of the agent's position. The method can also be easily generalized for a 3D scenario.

An intensity-enhanced LiDAR SLAM for unstructured environments

Traditional LiDAR simultaneous localization and mapping (SLAM) methods rely on geometric features such as lines and planes to estimate pose. However, in unstructured environments where geometric features are sparse or absent, point cloud registration may fail, resulting in decreased mapping and localization accuracy of the LiDAR SLAM system. To overcome this challenge, we propose a comprehensive LiDAR SLAM framework that leverages both geometric and intensity information, specifically tailored for unstructured environments. Firstly, we adaptively extract intensity features and construct intensity constraints based on degradation detection, and then propose a multi-resolution intensity map construction method. The experimental results show that our method achieves a 55% accuracy improvement over the pure geometric LiDAR SLAM system and exhibits superior anti-interference capability in urban corner scenarios. Compared with Intensity-SLAM, the advanced intensity-assisted LiDAR SLAM, our method achieves higher accuracy and efficiency.

A LiDAR-intensity SLAM and loop closure detection method using an intensity cylindrical-projection shape context descriptor

LiDAR Simultaneous Localization and Mapping (SLAM) is able to map unknown scene while online estimating the LiDAR’s pose. Traditional LiDAR SLAM frameworks mainly rely on geometric features in the environments, but ignore LiDAR intensity information, leading to low accuracy in scenes with sparse environmental features. This paper proposes a novel intensity-based LiDAR-SLAM framework. In the front-end, the geometric and intensity features are extracted to match the two consecutive scans. To keep time efficiency, a self-adaptive feature selection strategy is proposed to select geometric and intensity features and the 6 DOF transformation from the corresponding features are weighted fused for odometry estimation. To improve the effectiveness of loop closure detection (LCD), we propose a novel intensity cylindrical-projection shape context (ICPSC) descriptor and a row-column similarity estimation based on ICPSC. To guarantee the accuracy of LCD, a double-value loop candidate verification strategy is employed. We have conducted comprehensive experimental verification of our LiDAR SLAM framework, including both the public KITTI datasets and data from our platform that involve multiple scenes. The results display that our framework achieves an averaged improvement of about 3 m in scenes with sparse environmental features in terms of on-line localization relative to LeGO-LOAM, while only increases maximum 9 ms in terms of time cost.

Research on Multi-Sensor Simultaneous Localization and Mapping Technology for Complex Environment of Construction Machinery

Simultaneous localization and mapping (SLAM), as a key task of unmanned vehicles for construction machinery, is of great significance for later path planning and control. Construction tasks in the engineering field are mostly carried out in bridges, tunnels, open fields, etc. The prominent features of these environments are high scene similarity, few geometric features, and large-scale repetitive texture information, which is prone to sensor detection degradation. This leads to positioning drift and map building failure. The traditional method of motion estimation and 3D reconstruction uses a single sensor, which lacks enough information, has poor adaptability to the environment, and cannot guarantee good positioning accuracy and robustness in complex environments. Currently, the strategy of multi-sensor fusion is proven to be an effective solution and is widely studied. This paper proposes a SLAM framework that integrates LiDAR, IMU, and camera. It tightly couples the texture information observed by camera, the geometric information scanned by LiDAR, and the measured value of IMU, allowing visual-inertial odometry (VIO) and LiDAR-inertial odometry (LIO) common implementation. The LIO subsystem extracts point cloud features and matches them with the global map. The obtained pose estimation can be used for the initialization of the VIO subsystem. The VIO system uses direct method to minimize the photometric error and IMU measurement error between images to estimate the pose of the robot and the geometric structure of the scene. The two subsystems assist each other to perform pose estimation, and can operate normally even when any subsystem fails. A factor graph is used to combine all constraints to achieve global pose optimization. Keyframe and sliding window strategies are used to ensure real-time performance. Through real-vehicle testing, the system can perform incremental and real-time state estimation and reconstruct a dense 3D point cloud map, which can effectively solve the problems of positioning drift and mapping failure in the lack of geometric features or challenging construction environments.

NR5G-SAM: A SLAM Framework for Field Robot Applications Based on 5G New Radio.

Robot localization is a crucial task in robotic systems and is a pre-requisite for navigation. In outdoor environments, Global Navigation Satellite Systems (GNSS) have aided towards this direction, alongside laser and visual sensing. Despite their application in the field, GNSS suffers from limited availability in dense urban and rural environments. Light Detection and Ranging (LiDAR), inertial and visual methods are also prone to drift and can be susceptible to outliers due to environmental changes and illumination conditions. In this work, we propose a cellular Simultaneous Localization and Mapping (SLAM) framework based on 5G New Radio (NR) signals and inertial measurements for mobile robot localization with several gNodeB stations. The method outputs the pose of the robot along with a radio signal map based on the Received Signal Strength Indicator (RSSI) measurements for correction purposes. We then perform benchmarking against LiDAR-Inertial Odometry Smoothing and Mapping (LIO-SAM), a state-of-the-art LiDAR SLAM method, comparing performance via a simulator ground truth reference. Two experimental setups are presented and discussed using the sub-6 GHz and mmWave frequency bands for communication, while the transmission is based on down-link (DL) signals. Our results show that 5G positioning can be utilized for radio SLAM, providing increased robustness in outdoor environments and demonstrating its potential to assist in robot localization, as an additional absolute source of information when LiDAR methods fail and GNSS data is unreliable.

Integrating Sparse Learning-Based Feature Detectors into Simultaneous Localization and Mapping-A Benchmark Study.

Simultaneous localization and mapping (SLAM) is one of the cornerstones of autonomous navigation systems in robotics and the automotive industry. Visual SLAM (V-SLAM), which relies on image features, such as keypoints and descriptors to estimate the pose transformation between consecutive frames, is a highly efficient and effective approach for gathering environmental information. With the rise of representation learning, feature detectors based on deep neural networks (DNNs) have emerged as an alternative to handcrafted solutions. This work examines the integration of sparse learned features into a state-of-the-art SLAM framework and benchmarks handcrafted and learning-based approaches by comparing the two methods through in-depth experiments. Specifically, we replace the ORB detector and BRIEF descriptor of the ORBSLAM3 pipeline with those provided by Superpoint, a DNN model that jointly computes keypoints and descriptors. Experiments on three publicly available datasets from different application domains were conducted to evaluate the pose estimation performance and resource usage of both solutions.

A Dynamic Scene Vision SLAM Method Incorporating Object Detection and Object Characterization

Simultaneous localization and mapping (SLAM) based on RGB-D cameras has been widely used for robot localization and navigation in unknown environments. Most current SLAM methods are constrained by static environment assumptions and perform poorly in real-world dynamic scenarios. To improve the robustness and performance of SLAM systems in dynamic environments, this paper proposes a new RGB-D SLAM method for indoor dynamic scenes based on object detection. The method presented in this paper improves on the ORB-SLAM3 framework. First, we designed an object detection module based on YOLO v5 and relied on it to improve the tracking module of ORB-SLAM3 and the localization accuracy of ORB-SLAM3 in dynamic environments. The dense point cloud map building module was also included, which excludes dynamic objects from the environment map to create a static environment point cloud map with high readability and reusability. Full comparison experiments with the original ORB-SLAM3 and two representative semantic SLAM methods on the TUM RGB-D dataset show that: the method in this paper can run at 30+fps, the localization accuracy improved to varying degrees compared to ORB-SLAM3 in all four image sequences, and the absolute trajectory accuracy can be improved by up to 91.10%. The localization accuracy of the method in this paper is comparable to that of DS-SLAM, DynaSLAM and the two recent target detection-based SLAM algorithms, but it runs faster. The RGB-D SLAM method proposed in this paper, which combines the most advanced object detection method and visual SLAM framework, outperforms other methods in terms of localization accuracy and map construction in a dynamic indoor environment and has a certain reference value for navigation, localization, and 3D reconstruction.

SLAM for Indoor Parking: A Comprehensive Benchmark Dataset and a Tightly Coupled Semantic Framework

For the task of autonomous indoor parking, various Visual-Inertial Simultaneous Localization And Mapping (SLAM) systems are expected to achieve comparable results with the benefit of complementary effects of visual cameras and the Inertial Measurement Units. To compare these competing SLAM systems, it is necessary to have publicly available datasets, offering an objective way to demonstrate the pros/cons of each SLAM system. However, the availability of such high-quality datasets is surprisingly limited due to the profound challenge of the groundtruth trajectory acquisition in the Global Positioning Satellite denied indoor parking environments. In this article, we establish BeVIS, a large-scale Be nchmark dataset with V isual (front-view), I nertial and S urround-view sensors for evaluating the performance of SLAM systems developed for autonomous indoor parking, which is the first of its kind where both the raw data and the groundtruth trajectories are available. In BeVIS, the groundtruth trajectories are obtained by tracking artificial landmarks scattered in the indoor parking environments, whose coordinates are recorded in a surveying manner with a high-precision Electronic Total Station. Moreover, the groundtruth trajectories are comprehensively evaluated in terms of two respects, the reprojection error and the pose volatility, respectively. Apart from BeVIS, we propose a novel tightly coupled semantic SLAM framework, namely VIS SLAM -2, leveraging V isual (front-view), I nertial, and S urround-view sensor modalities, specially for the task of autonomous indoor parking. It is the first work attempting to provide a general form to model various semantic objects on the ground. Experiments on BeVIS demonstrate the effectiveness of the proposed VIS SLAM -2. Our benchmark dataset BeVIS is publicly available at https://shaoxuan92.github.io/BeVIS .

A monocular SLAM system based on SIFT features for gastroscope tracking

During flexible gastroscopy, physicians have extreme difficulties to self-localize. Camera tracking method such as simultaneous localization and mapping (SLAM) has become a research hotspot in recent years, allowing tracking of the endoscope. However, most of the existing solutions have focused on tasks in which sufficient texture information is available, such as laparoscope tracking, and cannot be applied to gastroscope tracking since gastroscopic images have fewer textures than laparoscopic images. This paper proposes a new monocular SLAM framework based on scale-invariant feature transform (SIFT) and narrow-band imaging (NBI), which extracts SIFT features instead of oriented features from accelerated segment test (FAST) and rotated binary robust independent elementary features (BRIEF) features from gastroscopic NBI images, and performs feature retention based on the response sorting strategy for achieving more matches. Experimental results show that the root mean squared error of the proposed algorithm can reach a minimum of 2.074mm, and the pose accuracy can be improved by up to 25.73% compared with oriented FAST and rotated BRIEF (ORB)-SLAM. SIFT features and response sorting strategy can achieve more accurate matching in gastroscopic NBI images than other features and homogenization strategy, and the proposed algorithm can also run successfully on real clinical gastroscopic data. The proposed algorithm has the potential clinical value to assist physicians in locating the gastroscope during gastroscopy.

Nonparametric Background Model-Based LiDAR SLAM in Highly Dynamic Urban Environments

In urban environments, simultaneous localization and mapping (SLAM) are essential for autonomous driving. Most light detection and ranging (LiDAR) SLAM methodologies have been developed for relatively static environments, despite real-world environments having many dynamic objects such as vehicles, bicycles, and pedestrians. This paper proposes an efficient and robust LiDAR SLAM. Our SLAM framework leverages the estimated background model to achieve robust motion estimation in dynamic urban environments. Based on probabilistic object estimation, the dynamic removal module estimates a nonparametric background model to recognize dynamic objects. This module estimates the probability of the difference of the range values from the accumulated LiDAR frames. Then, dynamic objects are removed by adapting the sensor velocity from the estimated ego-motion. In the local mapping module, our method optimizes the LiDAR motion considering the dynamic characteristics of LiDAR point clouds. Finally, the proposed method results in a global map with static point clouds and accurate LiDAR motion with global pose optimization. We tested the proposed method on the well-known public dataset (KITTI) and the custom dataset with complex environments, including various moving objects. Comparisons with state-of-the-art (SOTA) methods demonstrate that the our approach is more robust and efficient. For example, the proposed method performed an average 0.63% and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.18^{\circ }/100\;m$ </tex-math></inline-formula> errors on the KITTI dataset with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.96ms$ </tex-math></inline-formula> processing time that convinces real-time processing.

A monocular visual SLAM system augmented by lightweight deep local feature extractor using in-house and low-cost LIDAR-camera integrated device

ABSTRACT Simultaneous Localization and Mapping (SLAM) has been widely used in emergency response, self-driving and city-scale 3D mapping and navigation. Recent deep-learning based feature point extractors have demonstrated superior performance in dealing with the complex environmental challenges (e.g. extreme lighting) while the traditional extractors are struggling. In this paper, we have successfully improved the robustness and accuracy of a monocular visual SLAM system under various complex scenes by adding a deep learning based visual localization thread as an augmentation to the visual SLAM framework. In this thread, our feature extractor with an efficient lightweight deep neural network is used for absolute pose and scale estimation in real time using the highly accurate georeferenced prior map database at 20cm geometric accuracy created by our in-house and low-cost LiDAR and camera integrated device. The closed-loop error provided by our SLAM system with and without this enhancement is 1.03m and 18.28m respectively. The scale estimation of the monocular visual SLAM is also significantly improved (0.01 versus 0.98). In addition, a novel camera-LiDAR calibration workflow is also provided for large-scale 3D mapping. This paper demonstrates the application and research potential of deep-learning based vision SLAM with image and LiDAR sensors.

A Variational Bayesian-Based Simultaneous Localization and Mapping Method for Autonomous Underwater Vehicle Navigation

Simultaneous Localization and Mapping (SLAM) is a well-known solution for mapping and realizing autonomous navigation of an Autonomous Underwater Vehicle (AUV) in unknown underwater environments. However, the inaccurate time-varying observation noise will cause filtering divergence and reduce the accuracy of localization and feature estimation. In this paper, VB-AUFastSLAM based on the unscented-FastSLAM (UFastSLAM) and the Variational Bayesian (VB) is proposed. The UFastSLAM combines unscented particle filter (UPF) and unscented Kalman filter (UKF) to estimate the AUV poses and features. In addition, to resist the unknown time-varying observation noise, the method of Variational Bayesian learning is introduced into the SLAM framework. Firstly, the VB method is used to estimate the joint posterior probability of the AUV path and observation noise. The Inverse-Gamma distribution is used to model the observation noise and real-time noise parameters estimation is performed to improve the AUV localization accuracy. Secondly, VB is reused to estimate the noise parameters in the feature update stage to enhance the performance of the feature estimation. The proposed algorithms are first validated in an open-source simulation environment. Then, an AUV SLAM system based on the Inertial Navigation System (INS), Doppler Velocity Log (DVL), and single-beam Sonar are also built to verify the effectiveness of the proposed algorithms in the marine environment. The accuracy of the proposed methods can reach 0.742% and 0.776% of the range, respectively, which is much better than 1.825% and 1.397% of the traditional methods.