Vision-Based Self-contained Target Following Robot Using Bayesian Data Fusion

Several visual following robots have been proposed in recent years. However, many require the use of several, expensive sensors and often the majority of the image processing and other calculations are performed off-board. This paper proposes a simple and cost effective, yet robust visual following robot capable of tracking a general object with limited restrictions on target characteristics. To detect the objects, tracking-learning-detection (TLD) is used within a Bayesian framework to filter and fuse the measurements. A time-of-flight (ToF) depth camera is used to refine the distance estimates at short ranges. The algorithms are executed in real-time (approximately 30fps) in a Jetson TK1 embedded computer. Experiments were conducted with different target objects to validate the system in scenarios including occlusions and various illumination conditions as well as to show how the data fusion between TLD and the ToF camera improves the distance estimation.