Real-time detection of moving objects in a dynamic scene from moving robotic vehicles

Dynamic scene perception is currently limited to detection of moving objects from a static platform or scenes with flat backgrounds. We discuss novel methods to segment moving objects in the motion field formed by a moving camerahobotic platform in real time. Our solution does not require any egomotion knowledge, thereby making the solution applicable to a large class of mobile outdoor robot problems where no IMU information is available. We address two of the toughest problems in dynamic scene perception on the move, first using only 2D monocular grayscale images, and second where 3 0 range information +om stereo is also available. Our solution involves real-time optical flow computations, followed by optical flow field preprocessing to highlight moving object boundaries. In the case where range data from stereo is computed, a 3 0 optical flow field is estimated by combining range information with 2 0 optical flow estimates, followed by a similar 3 0 flow field preprocessing step. A segmentation of the flow field using fast floodfilling then identifies every moving object in the scene with a unique label. This novel algorithm is expected to be the criticalfirst step in robust recognition of moving vehicles and people from mobile outdoor robots, and therefore offers a general solution to the field of dynamic scene perception. It is envisioned that our algorithm will benefit robot scene perception in urban environments for scientific, commercial and defense applications. Results of our real-time algorithm on a mobile robot in a scene with a single moving vehicle are pre-