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

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.

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Publication Details

Published
2024-12-04
DOI
https://doi.org/10.1145/3636534.3694721
Citations
3
Primary Topic
Robotics and Sensor-Based Localization
Type
article
Field-Weighted Citation Impact
3.90
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article

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

Jingao Xu, Xinlei Chen, Chenxin Liang, Haoyang Wang et al.
3 citations
Robotics and Sensor-Based Localization
3.90
article

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

Jingao Xu, Xinlei Chen, Chenxin Liang, Haoyang Wang, Ciyu Ruan, Xinyu Luo
article en
3 citations

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.

University Town of Shenzhen (CN), Software (Spain) (ES), Tsinghua University (CN)
Openalex Percentile: Top 6%
Robotics and Sensor-Based Localization
3.90
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EventTracker: 3D Localization and Tracking of High-Speed Object with Event and Depth Fusion — Jingao Xu, Xinlei Chen, et al. · (2024) | TGRS Research Map | TGRS