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Efficient Obstacle Detection and Tracking Using RGB-D Sensor Data in Dynamic Environments for Robotic Applications.

Arindam Saha1, Bibhas Chandra Dhara1, Saiyed Umer2

  • 1Department of Information Technology, Jadavpur University, Kolkata 700098, India.

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Summary
This summary is machine-generated.

This study introduces an efficient method for robot obstacle detection and tracking using depth images. The novel approach enhances accuracy and speed in dynamic environments, outperforming existing vision-based techniques.

Keywords:
RGB-Ddynamic obstacle estimationobstacle detectionrobotu-depth mapv-depth map

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Area of Science:

  • Robotics
  • Computer Vision
  • Artificial Intelligence

Background:

  • Autonomous navigation relies heavily on accurate obstacle detection.
  • Dynamic and cluttered environments pose significant challenges for robot perception.
  • RGB-D cameras are crucial for real-time environmental sensing, providing both color and depth information.

Purpose of the Study:

  • To propose an efficient obstacle detection and tracking method using depth images for autonomous robots.
  • To enhance the accuracy and speed of dynamic obstacle detection in complex environments.
  • To improve the state estimation and tracking stability of detected obstacles.

Main Methods:

  • Utilized a u-depth map for initial obstacle detection, incorporating dynamic thresholding for improved accuracy.
  • Implemented a restricted v-depth map technique for precise obstacle dimension prediction via post-processing.
  • Developed a novel algorithm for robust obstacle tracking within the robot's field of view (FOV).

Main Results:

  • The proposed method demonstrated superior performance in state estimation of dynamic obstacles compared to state-of-the-art (SoA) vision-based methods.
  • Achieved significantly faster execution times, crucial for real-time autonomous navigation.
  • Successfully evaluated on diverse datasets, confirming robustness and efficiency.

Conclusions:

  • The developed method offers an efficient and accurate solution for obstacle detection and tracking in challenging robotic navigation scenarios.
  • Dynamic thresholding on u-depth maps and restricted v-depth map post-processing are key innovations for enhanced perception.
  • The system provides a reliable foundation for improving the safety and performance of autonomous robots.