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Sensor Fusion-Based Approach to Eliminating Moving Objects for SLAM in Dynamic Environments.

Xiangwei Dang1,2, Zheng Rong3, Xingdong Liang1,2

  • 1National Key Laboratory of Microwave Imaging Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China.

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Summary

This study evaluates Simultaneous Localization and Mapping (SLAM) in dynamic environments, finding moving objects degrade performance. A novel approach fusing LiDAR and radar effectively removes these objects, significantly improving SLAM accuracy and robustness.

Keywords:
LiDARSLAMdynamic environmentsmmW-radarmoving objectssensor fusion

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Area of Science:

  • Robotics and Autonomous Systems
  • Sensor Fusion
  • Simultaneous Localization and Mapping (SLAM)

Background:

  • Accurate robot localization and mapping are crucial for autonomous navigation.
  • Existing Simultaneous Localization and Mapping (SLAM) methods often fail in dynamic environments due to moving objects.
  • LiDAR-based SLAM performance is significantly impacted by the characteristics of moving objects.

Purpose of the Study:

  • To quantitatively evaluate the impact of moving objects on state-of-the-art LiDAR-based SLAM.
  • To propose a novel sensor fusion approach for eliminating moving objects in SLAM.
  • To enhance the accuracy and robustness of robot state estimation in dynamic environments.

Main Methods:

  • Quantitative evaluation of LiDAR-based SLAM with varying moving object patterns using semi-physical simulations.
  • Development of the EMO (Eliminating Moving Objects) approach, fusing LiDAR and mmWave radar data.
  • Detection of moving objects using radar's Doppler effect, segmentation and localization using LiDAR, followed by filtering and synchronization.

Main Results:

  • Moving object shape, size, and distribution significantly impact SLAM performance.
  • The proposed EMO method effectively detects, segments, and filters moving objects.
  • Experimental results show at least a 30% decrease in absolute position error for SLAM in dynamic environments.

Conclusions:

  • Moving objects pose a significant challenge to current SLAM systems.
  • Sensor fusion of LiDAR and mmWave radar offers a robust solution for dynamic environment SLAM.
  • The EMO approach substantially improves SLAM accuracy and robustness, enabling reliable autonomous navigation.