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

arXiv: https://arxiv.org/abs/2303.00132 · HTML: https://mlsanity.com/paper/2303.00132/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Feb 2023.

## Built on by (most cited first, 15 of 15)

- [NavRL: Learning Safe Flight in Dynamic Environments](https://mlsanity.com/paper/2409.15634/) (2024)
- [LiftFeat: 3D Geometry-Aware Local Feature Matching](https://mlsanity.com/paper/2505.03422/) (2025)
- [Beyond Kalman Filters: Deep Learning-Based Filters for Improved Object Tracking](https://mlsanity.com/paper/2402.09865/) (2024)
- [Engineering an Efficient Object Tracker for Non-Linear Motion](https://mlsanity.com/paper/2407.00738/) (2024)
- [Deep Learning-Based Multi-Object Tracking: A Comprehensive Survey from Foundations to State-of-the-Art](https://mlsanity.com/paper/2506.13457/) (2025)
- [OpenNav: Efficient Open Vocabulary 3D Object Detection for Smart Wheelchair Navigation](https://mlsanity.com/paper/2408.13936/) (2024)
- [NavRL++: A System-Level Framework for Improving Sim-to-Real Transfer in Reinforcement Learning-Based Robot Navigation](https://mlsanity.com/paper/2605.15559/) (2026)
- [Collaborative Reinforcement Learning Based Unmanned Aerial Vehicle (UAV) Trajectory Design for 3D UAV Tracking](https://mlsanity.com/paper/2401.12079/) (2024)
- [Person Segmentation and Action Classification for Multi-Channel Hemisphere Field of View LiDAR Sensors](https://mlsanity.com/paper/2411.11151/) (2024)
- [Object segmentation in the wild with foundation models: application to vision assisted neuro-prostheses for upper limbs](https://mlsanity.com/paper/2507.18517/) (2025)
- [Learning Association via Track-Detection Matching for Multi-Object Tracking](https://mlsanity.com/paper/2512.22105/) (2025)
- [M2I2HA: Multi-modal Object Detection Based on Intra- and Inter-Modal Hypergraph Attention](https://mlsanity.com/paper/2601.14776/) (2026)
- [Fore-Mamba3D: Mamba-based Foreground-Enhanced Encoding for 3D Object Detection](https://mlsanity.com/paper/2602.19536/) (2026)
- [Revisiting Multi-Object Tracking Baselines: Hyperparameter Optimization with Multi-Fidelity Greedy Coordinate Search](https://mlsanity.com/paper/2609.12261/) (2026)
- [Image Frame Dynamic Object Segmentation and Ego Motion Estimation using Radar Image Fusion](https://mlsanity.com/paper/2609.22857/) (2026)

## Abstract (arXiv)

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.