Onboard dynamic-object detection and tracking for autonomous robot navigation with RGB-D camera

TL;DR

This paper presents a lightweight, real-time 3D dynamic obstacle detection and tracking system using RGB-D cameras, tailored for small, low-power robots like UAVs, with demonstrated accuracy and efficiency in indoor navigation.

Contribution

The authors introduce a novel ensemble detection strategy and feature-based data association for RGB-D obstacle detection on low-power robots, including an optional learning module for improved range.

Findings

Achieves 0.11m position error in onboard experiments

Maintains comparable velocity error of 0.23m/s

Enables efficient dynamic obstacle navigation in real-world tests

Abstract

Deploying autonomous robots in crowded indoor environments usually requires them to have accurate dynamic obstacle perception. Although plenty of previous works in the autonomous driving field have investigated the 3D object detection problem, the usage of dense point clouds from a heavy Light Detection and Ranging (LiDAR) sensor and their high computation cost for learning-based data processing make those methods not applicable to small robots, such as vision-based UAVs with small onboard computers. To address this issue, we propose a lightweight 3D dynamic obstacle detection and tracking (DODT) method based on an RGB-D camera, which is designed for low-power robots with limited computing power. Our method adopts a novel ensemble detection strategy, combining multiple computationally efficient but low-accuracy detectors to achieve real-time high-accuracy obstacle detection. Besides, we introduce a new feature-based data association and tracking method to prevent mismatches utilizing point clouds' statistical features. In addition, our system includes an optional and auxiliary learning-based module to enhance the obstacle detection range and dynamic obstacle identification. The proposed method is implemented in a small quadcopter, and the results show that our method can achieve the lowest position error (0.11m) and a comparable velocity error (0.23m/s) across the benchmarking algorithms running on the robot's onboard computer. The flight experiments prove that the tracking results from the proposed method can make the robot efficiently alter its trajectory for navigating dynamic environments. Our software is available on GitHub as an open-source ROS package.