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 real-time methods to segment moving objects in the motion field formed by a moving camera/robotic platform with mostly translational motion. Our solution does not explicitly require any egomotion knowledge, thereby making the solution applicable to mobile outdoor robot problems where no IMU information is available. We address two problems in dynamic scene perception on the move, first using only 2D monocular grayscale images, and second where 3D range information from 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 3D optical flow field is estimated by combining range information with 2D optical flow estimates, followed by a similar 3D flow field preprocessing step. A segmentation of the flow field using fast flood filling then identifies every moving object in the scene with a unique label. This novel algorithm is expected to be the critical first step in robust recognition of moving vehicles and people from mobile outdoor robots, and therefore offers a robust solution to the problem of dynamic scene perception in the presence of certain kinds of robot motion. 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 presented.