A Bayesian Recursive Algorithm for Freespace Estimation Using a Stereoscopic Camera System in an Autonomous Wheelchair

This paper proposes a Bayesian Recursive (BR) algorithm for detecting freespace for people with severe disability operating an autonomous wheelchair with a stereoscopic camera system. Based on the left and right images captured from the camera system, the Sum of Absolute Differences (SAD) algorithm is used to produce a stereo disparity map. In short, a three-dimensional (3D) point map is converted to a two-dimensional (2D) distance map. This is generated from the disparity map using a geometric projection. Being based on uncertain information captured from the camera system, the BR algorithm for freespace estimation is developed as a recursive expression for the posterior probability function. The average probability values are calculated for the height and width of the freespace, and these assist to plan a power wheelchair pathway. Based on the average probability of the estimated height and width of the freespace, a Bayesian decision for the power wheelchair to autonomously navigate through the freespace is made. Moreover, Obstacle avoidance problem based on 2D map is considered in this paper. As further support for the method, experimental results indicate the effectiveness of the proposed algorithm.