Detection of obstacles in the path planning module using differential scene flow technique

The paper shows the novel way of detecting the obstacles, so that the intelligence of the autonomous vehicle (AV) can be enhanced for the path planning module. Path planning in cluttered environment has been considered as the main challenge in the field of autonomous vehicles. These vehicles need to be intelligent enough to recognize their surroundings and avoid the obstacles effectively. In cluttered environment, the AV requires the comprehensive perception of the environment to avoid the obstacles. So it is better to know about the kinematic behaviour of the obstacles while planning their path. Hence, the orientation of moving obstacle is estimated based on comparing the 3D range images between successive time frames. These 3D range information are acquired from a single vision sensor. The paper uses the differential scene flow technique to extract the flow vectors in 3D coordinates. Gradient Vector Field (GVF) method is utilized effectively in scene flow to extract changes in pixel values in the three directions which are used to recognize the obstacles. Our aim is to develop an autonomous path planning algorithm with closed-loop motion control based on scene flow which rely on a single vision sensor and an on-board hardware. Experimental results are discussed finally.