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DOT-SLAM: A Stereo Visual Simultaneous Localization and Mapping (SLAM) System with Dynamic Object Tracking Based on

Yuan Zhu1, Hao An1, Huaide Wang1

  • 1School of Automotive Studies, Tongji University, Shanghai 201800, China.

Sensors (Basel, Switzerland)
|July 27, 2024
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Summary
This summary is machine-generated.

This study introduces DOT-SLAM, a visual SLAM system that improves autonomous vehicle localization accuracy by tracking dynamic objects. It effectively uses both static and moving features for precise ego-vehicle pose estimation.

Keywords:
dynamic scenegraph optimizationnon-holonomic constraintobject trackingstereo visual SLAM

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Area of Science:

  • Robotics
  • Computer Vision
  • Autonomous Systems

Background:

  • Visual SLAM systems often assume static environments, leading to reduced accuracy with dynamic objects.
  • Dynamic objects, especially vehicles, significantly degrade localization performance in autonomous driving.

Purpose of the Study:

  • To develop a novel stereo visual SLAM system (DOT-SLAM) that integrates dynamic object tracking.
  • To enhance ego-vehicle localization accuracy and generate static environment maps in the presence of dynamic objects.

Main Methods:

  • Integrated dynamic object pose estimation using graph optimization.
  • Employed a coarse-to-fine depth estimation method leveraging camera-road plane geometry.
  • Utilized road plane and non-holonomic constraints for accurate dynamic object initialization.
  • Jointly optimized ego-vehicle pose, dynamic object poses, foreground/background points, and road plane via graph optimization.

Main Results:

  • DOT-SLAM effectively utilizes features from both dynamic objects and the background.
  • Achieved more accurate vehicle trajectory estimation compared to traditional methods.
  • Generated a static environment map, demonstrating improved localization performance.
  • Validated effectiveness on the KITTI-360 dataset and real-world tests.

Conclusions:

  • DOT-SLAM significantly improves localization accuracy in dynamic environments for autonomous vehicles.
  • The system successfully integrates dynamic object information for robust SLAM.
  • Offers a promising solution for reliable navigation in complex, real-world scenarios.