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A Real-time visual slam towards Dynamic Scenes Based on YOLACT

A Real-time Visual SLAM towards Dynamic Scenes Based on YOLA...

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第43届中国控制会议

作者： Zhongyu Li Jun Yang Ziwen Wei School of Information Science and Engineering Wuhan University of Science and Technology

The current research on the stability of visual slam in dynamic scenes is in the stage of refining the *** there are better algorithms in static scenes,how to make it able to operate stably in dynamic scenes is a challenging *** paper proposes a visual slam based on YOLACT with pixel-level dynamic semantic segmentation *** rejects potential dynamic targets in the image through a real-time instance segmentation model,ensures that all feature points extracted by the front-end are from static objects,and avoids the adverse effect of abnormal dynamic feature points on the position *** real-time performance of the system can be ensured due to the addition of instance segmentation threads that are independent of the tracking *** evaluate the performance of the system in various scenarios in two public datasets,KITTI and TUM,and experiment is conducted in real *** experimental results show that the system not only ensures real-time performance but also improves the localization accuracy in dynamic scenarios.

关键词： visual-slam Dynamic scenes Instance segmentation Feature extraction Real-time system

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SLI-slam: Autonomous Navigation and Accurate Mapping for Quadruped Robot in Complex Environments Using LiDAR,Stereo Camera, and IMU Fusion

SLI-SLAM: Autonomous Navigation and Accurate Mapping for Qua...

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第43届中国控制会议

作者： Yiming Xu Bin Li Shuhui Yang Yumin Zhi Rukun Li School of Electronics Electrical and Control Qilu University of Technology (Shandong Academy of Sciences) School of Mathematics and Statistics Qilu University of Technology (Shandong Academy of Sciences)

The proposed algorithm, SLI-slam(Stereo Camera-LiDAR-Inertial Measurement unit Fusion slam), introduces a comprehensive multi-sensor fusion framework for achieving navigation capabilities in quadruped robots. It combines stereo cameras, LiDAR, and IMU sensors using the factor graph framework for simultaneous localization and mapping(slam). It incorporates three types of odometry(LiDAR, visual, and IMU) and effectively fuse their data to improve system *** algorithm enables autonomous perception, navigation, obstacle avoidance, and path planning. During autonomous navigation, the robot continuously perceives the environment, updates its position, and dynamically adjusts the planned path to navigate safely around obstacles. The SLI-slam algorithm, in combination with the NDT and GMapping algorithms, facilitates real-time map updates, allowing for adaptation to environmental changes. The SLI-slam algorithm has been extensively validated on the popular KITTI dataset and in real-world environments. Compared to other slam approaches in complex scenarios, the comparative analysis demonstrates its accuracy and robustness. Despite challenges in implementation, such as sensor calibration,synchronization, noise, and computational complexity, the SLI-slam algorithm has successfully been applied to quadruped robots and extensively tested, confirming its effectiveness and practicality. The SLI-slam algorithm enhances robustness, realtime performance, and the integration of environmental information, leading to accurate localization and mapping in complex scenarios.

关键词： Quadruped robot Multi-sensor fusion visual-slam Li DAR-slam Navigation Map construction

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