EventTracker: 3D Localization and Tracking of High-Speed Object with Event and Depth Fusion

Xinyu Luo, Haoyang Wang, Ciyu Ruan, Chenxin Liang, Jingao Xu, Xinlei Chen

Abstract: Accurately localizing high-speed dynamic objects in 3D space with low latency is crucial for various robotic applications. Current methods face challenges due to extended exposure times and limited sensor resolution, hindering precise object detection and localization. Event cameras, known for their high temporal resolution and asynchronous nature, offer a promising solution. To leverage the potential of the event camera, we propose EventTracker, a novel framework that integrates event and depth measurements for precise and low-latency 3D localization and tracking of the high-speed dynamic object. EventTracker incorporates a collaborative object detection and tracking algorithm optimized for both event and depth data, overcoming detection and registration challenges. Additionally, a graph-instructed optimization algorithm enhances accuracy by fusing heterogeneous sensor data effectively. Experimental evaluation in dynamic environments demonstrates significant improvements in localization performance compared to baseline methods.

External IDs:doi:10.1145/3636534.3694721