This paper presents a real-time method to detect and track multiple mobile ground robots using event cameras. The method uses density-based spatial clustering of applications with noise (DBSCAN) to detect the robots and a single k-dimensional ($k - d$) tree to accurately keep track of them as they move in an indoor arena. Robust detections and tracks are maintained in the face of event camera noise and lack of events (due to robots moving slowly or stopping). An off-the-shelf RGB camera-based tracking system was used to provide ground truth. Experiments including up to 4 robots are performed to study the effect of i) varying DBSCAN parameters, ii) the event accumulation time, iii) the number of robots in the arena, iv) the speed of the robots, and v) variation in ambient light conditions on the detection and tracking performance. The experimental results showed 100% detection and tracking fidelity in the face of event camera noise and robots stopping for tests involving up to 3 robots (and upwards of 93% for 4 robots). When the lighting conditions were varied, a graceful degradation in detection and tracking fidelity was observed.

翻译：本文介绍了使用事件相机探测和跟踪多个移动地面机器人的实时方法。该方法使用以密度为基础的空间组合方式,对有噪音的应用(DBSCAN)进行基于密度的空间分组(DBSCAN),以检测机器人和单K维(k-d$)树,准确跟踪机器人在室内运动的距离。当发生相机噪音和缺乏事件时(由于机器人缓慢移动或停止运行)保持强力探测和跟踪轨道。使用了现成的RGB相机跟踪系统,以提供地面真实性。进行了包括最多4个机器人在内的实验,以研究i)不同DBSCAN参数的效果,二)事件累积时间,三)舞台上机器人的数量,四)机器人的速度,以及探测和跟踪性能的环境光度条件的变化。实验结果表明,在事件摄像机噪音和机器人面前100%的探测和跟踪准确性,停止测试的机器人多达3个机器人(4个机器人上升93%)。当照明条件变化时,检测和跟踪的优劣度被观察到。