FAST-Dynamic-Vision: Detection and Tracking Dynamic Objects with Event and Depth Sensing

TL;DR

This paper introduces a perception system for UAVs that combines event and depth sensing to detect and track fast-moving objects accurately and with low latency, enhancing aerial autonomy in complex environments.

Contribution

It presents a novel perception framework integrating ego-motion compensation, event-based detection, and asynchronous fusion for trajectory prediction of dynamic objects.

Findings

High-precision detection of fast-moving objects

Robust ego-motion compensation considering rotational and translational motion

Effective asynchronous fusion of event and depth data

Abstract

The development of aerial autonomy has enabled aerial robots to fly agilely in complex environments. However, dodging fast-moving objects in flight remains a challenge, limiting the further application of unmanned aerial vehicles (UAVs). The bottleneck of solving this problem is the accurate perception of rapid dynamic objects. Recently, event cameras have shown great potential in solving this problem. This paper presents a complete perception system including ego-motion compensation, object detection, and trajectory prediction for fast-moving dynamic objects with low latency and high precision. Firstly, we propose an accurate ego-motion compensation algorithm by considering both rotational and translational motion for more robust object detection. Then, for dynamic object detection, an event camera-based efficient regression algorithm is designed. Finally, we propose an optimizationbased approach that asynchronously fuses event and depth cameras for trajectory prediction. Extensive real-world experiments and benchmarks are performed to validate our framework. Moreover, our code will be released to benefit related researches.