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, v) variation in ambient light conditions on the detection and tracking performance, and vi) the effect of alternative clustering algorithms on detection 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.
All Science Journal Classification (ASJC) codes
- Computer Science(all)
- Materials Science(all)
- Event cameras
- clustering and pattern recognition
- detection and tracking
- multi-robot systems