Robust Stereo Visual SLAM for Dynamic Environments With Moving Object

Li, Gang; Liao, Xiang; Huang, Huilan; Song, Shaojian; Liu, Bin; Zeng, Yawen

doi:10.1109/access.2021.3059866

Cited by 18 publications

(10 citation statements)

References 33 publications

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“…YOLACT divides instance segmentation into two parallel tasks: Generating a non-local prototype mask over the entire image and predicting a set of linear combination coefficients per instance. It then generates a full-image instance segmentation from these two components: For each instance, the prototype is linearly combined with the corresponding prediction coefficient, then the predicted bounding box is used for cropping [ 8 ].…”

Section: Methodsmentioning

confidence: 99%

“…However, in reality there are not many completely static scenes; most scenes contain dynamic objects which will occlude the features being tracked, which may result in reduced accuracy of pose solution or loss of system tracking. Therefore, improving the accuracy and stability of SLAM in dynamic scenarios is an important research direction [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Semantic SLAM System for Catadioptric Panoramic Cameras in Dynamic Environments

Zhang

Ning

et al. 2021

Sensors

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When a traditional visual SLAM system works in a dynamic environment, it will be disturbed by dynamic objects and perform poorly. In order to overcome the interference of dynamic objects, we propose a semantic SLAM system for catadioptric panoramic cameras in dynamic environments. A real-time instance segmentation network is used to detect potential moving targets in the panoramic image. In order to find the real dynamic targets, potential moving targets are verified according to the sphere’s epipolar constraints. Then, when extracting feature points, the dynamic objects in the panoramic image are masked. Only static feature points are used to estimate the pose of the panoramic camera, so as to improve the accuracy of pose estimation. In order to verify the performance of our system, experiments were conducted on public data sets. The experiments showed that in a highly dynamic environment, the accuracy of our system is significantly better than traditional algorithms. By calculating the RMSE of the absolute trajectory error, we found that our system performed up to 96.3% better than traditional SLAM. Our catadioptric panoramic camera semantic SLAM system has higher accuracy and robustness in complex dynamic environments.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Semantic SLAM System for Catadioptric Panoramic Cameras in Dynamic Environments

Zhang

Ning

et al. 2021

Sensors

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show abstract

“…Study [ 7 ] proposed an NDT-based SLAM method, which can achieve long-range high-precision map establishment and localization in dynamic scenarios. Li et al [ 18 ] improved the accuracy of stereo visual SLAM by removing dynamic obstacles. Wen et al [ 19 ] compared the performance of NDT-based graph optimization SLAM under complex urban conditions; the results show that the performance of the NDT SLAM algorithm is positively related to the traffic environment.…”

Section: Related Workmentioning

confidence: 99%

LiDAR-Based Sensor Fusion SLAM and Localization for Autonomous Driving Vehicles in Complex Scenarios

Dai

Sun

et al. 2023

J. Imaging

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LiDAR-based simultaneous localization and mapping (SLAM) and online localization methods are widely used in autonomous driving, and are key parts of intelligent vehicles. However, current SLAM algorithms have limitations in map drift and localization algorithms based on a single sensor have poor adaptability to complex scenarios. A SLAM and online localization method based on multi-sensor fusion is proposed and integrated into a general framework in this paper. In the mapping process, constraints consisting of normal distributions transform (NDT) registration, loop closure detection and real time kinematic (RTK) global navigation satellite system (GNSS) position for the front-end and the pose graph optimization algorithm for the back-end, which are applied to achieve an optimized map without drift. In the localization process, the error state Kalman filter (ESKF) fuses LiDAR-based localization position and vehicle states to realize more robust and precise localization. The open-source KITTI dataset and field tests are used to test the proposed method. The method effectiveness shown in the test results achieves 5–10 cm mapping accuracy and 20–30 cm localization accuracy, and it realizes online autonomous driving in complex scenarios.

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“…The vehicle's position is estimated using triangulation with centimetre-level accuracy. Another commonly used approach for AGV navigation is to use light detection and ranging (LiDAR) and inertial motion unit (IMU) measurements [60] or image-based (visual) localisation [61] together with simultaneous localisation and mapping (SLAM) algorithms.…”

Section: Indoor Positioning Technologiesmentioning

confidence: 99%

Precision Positioning for Smart Logistics Using Ultra-Wideband Technology-Based Indoor Navigation: A Review

et al. 2022

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Logistics is an important driver for the competitiveness of industries and material supply. The development of smart logistics, powered by precise positioning and communication technologies can significantly improve the efficiency of logistics. The emerging technology of ultra-wideband (UWB) precision positioning has attracted significant attention throughout the previous decade owing to its promising capabilities over other radio frequency-based indoor localisation systems. In addition, UWB is characterised by large bandwidth and data rate, short message length, low transmission power and high penetration capability, which are all favourable for indoor positioning applications. However, UWB localisation technology faces several challenges that are somewhat similar to other technologies, such as mitigating errors that originate from non-line-of-sight (NLOS) situations and tackling signal interference in dense environments, and when required to operate in extreme conditions. This paper reviews the most recent advances made in UWB positioning systems over the last five years, with a focus on high-ranking articles. In addition to going through more conventional solutions to UWB challenges, modern solutions, which involve the use of machine learning and sensor data fusion, are discussed. We highlight the most promising findings of the recently implemented and foreseen UWB positioning systems by providing a summary of each reviewed article. Additionally, we address a major challenge that faces the UWB positioning technology: NLOS situations, focusing on some proposed remedies such as multi-sensor fusion and machine learning. As an application, this study introduces how UWB technology promotes smart logistics by offering indoor positioning to improve efficiencies in the delivery of goods from the source to the customer. Furthermore, it demonstrates the benefits of UWB technology for accurate positioning and tracking of both stationary and moving items, and machinery in an indoor logistics environment.

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Robust Stereo Visual SLAM for Dynamic Environments With Moving Object

Cited by 18 publications

References 33 publications

A Semantic SLAM System for Catadioptric Panoramic Cameras in Dynamic Environments

A Semantic SLAM System for Catadioptric Panoramic Cameras in Dynamic Environments

LiDAR-Based Sensor Fusion SLAM and Localization for Autonomous Driving Vehicles in Complex Scenarios

Precision Positioning for Smart Logistics Using Ultra-Wideband Technology-Based Indoor Navigation: A Review

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