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A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
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Dynamic SLAM by Combining Rigid Feature Point Set Modeling and YOLO.

Pengchao Ding1, Weidong Wang1, Xian Wu1

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Summary

This study introduces a real-time dynamic visual-inertial SLAM algorithm for accurate localization in changing environments. It effectively segments objects and utilizes their motion for improved performance.

Keywords:
dynamic SLAMobject detectionvisual SLAM

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Area of Science:

  • Robotics
  • Computer Vision
  • Artificial Intelligence

Background:

  • Accurate localization in dynamic environments is crucial for autonomous systems.
  • Existing Simultaneous Localization and Mapping (SLAM) algorithms struggle with moving objects.

Purpose of the Study:

  • To develop a real-time dynamic visual-inertial SLAM algorithm capable of handling dynamic environments.
  • To improve the accuracy and robustness of SLAM in the presence of moving objects.

Main Methods:

  • Combined YOLO-V5 and depth thresholding for real-time pixel-level object segmentation.
  • K-means clustering for object depth extraction to handle occlusions.
  • Factor graph optimization incorporating rigid and non-rigid dynamic object motion.
  • Kalman filter for object matching and tracking.
  • Adaptive rigid point set modeling to enhance rigid object detection.

Main Results:

  • Demonstrated real-time performance in dynamic environments.
  • Successfully segmented and tracked dynamic objects, even when occluded.
  • Improved localization accuracy by effectively utilizing dynamic object motion information.

Conclusions:

  • The proposed dynamic visual-inertial SLAM algorithm effectively addresses challenges in dynamic environments.
  • The integration of object detection, depth extraction, and advanced optimization significantly enhances SLAM performance.
  • Validated through experiments on public and self-built datasets.