Mr. Yundong Mei Profile

Yundong Mei

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Proceedings Article | 13 September 2024 Paper

Review of multi-sensor fusion SLAM for mobile robot

Jun Dai, Yingying Lei, Junwei Zhao, Yundong Mei

Proceedings Volume 13178, 1317814 (2024) https://doi.org/10.1117/12.3032586

KEYWORDS: LIDAR, Cameras, Satellite navigation systems, Sensors, Point clouds, Visualization, Signal filtering, Error analysis, Tunable filters, Sensor fusion

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Simultaneous localization and mapping (SLAM) is a significant challenge for autonomous mobile robotics, as it necessitates the reconstruction of an unfamiliar environment while simultaneously determining the robot's position using a map. The mobile robot employed a diverse range of sensors in the SLAM process to gather and interpret a map representation. Geometric model-based strategies have historically been employed for addressing SLAM problems. However, these techniques are prone to errors in tough environments, and single-sensor SLAM systems have degradation and adverse effects on localization and mapping outcomes in extreme environments characterized by high dynamics or coefficient characteristics. Recently, there has been a significant increase in the development of multi-sensor fusion SLAM techniques aimed at achieving more stable and resilient systems. This paper focuses on the development process and contemporary research conducted on multi-sensor fusion SLAM. This paper initially presents the overall structure of SLAM. It then provides a detailed explanation of the functions of front-end odometry, back-end optimization, loopback detection, and map building modules. Additionally, it provides a summary of the algorithms employed in SLAM. Furthermore, it describes and summarizes the classical representative open source algorithms based on the types of sensors to be combined. It also introduces commonly utilized public datasets, along with accuracy evaluation indexes and measurement tools. In conclusion, this paper presents a comprehensive examination of the development process, along with a summary of prominent research studies on the fusion of many sensors in SLAM.

Proceedings Article | 13 September 2024 Paper

SDG-SLAM: an improved visual semantic SLAM algorithm towards dynamic environments

Jun Dai, Minghao Yang, Junwei Zhao, Yundong Mei

Proceedings Volume 13178, 131780W (2024) https://doi.org/10.1117/12.3032516

KEYWORDS: Semantics, Visualization, Cameras, Detection and tracking algorithms, Optical flow, Pose estimation, Feature extraction, Environmental sensing, Tunable filters, Optical tracking

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Visual simultaneous localization and mapping (SLAM) in dynamic scenarios is vulnerable to dynamic object factors, which can cause inaccurate pose estimation and limited robustness. To tackle this problem, we present a SLAM algorithm named SDG-SLAM, which combines improved visual semantic and geometric constraints to reject outliers and enhance performance. Firstly, the algorithm utilizes the neural network YOLOv5 (the fifth version of You Only Look Once) to obtain object’s prior semantics in the environment. In the meantime, the ORB feature points of keyframes are acquired, and the sparse optical flow tracking and matching are performed on non-key frames Secondly, adaptive initial dynamic point detection is implemented based on the epipolar constraints. Subsequently, the method of motion constraint posterior combined with semantic prior was used to screen and eliminate dynamic feature points. Finally, all static feature points from the environment are utilized, and these feature points are participated in the camera’s pose estimation. Substantial experiments performed on challenging public dynamic scenario datasets and outdoor dynamic scenarios demonstrate that SDG-SLAM significantly improves pose accuracy in scenes containing objects with different motion states. Specifically, compared to ORB-SLAM3, SDG-SLAM achieves more than 87% improvement in localization accuracy for high dynamic datasets and over 85% improvement for outdoor dynamic environments.

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