Commodity real-time stereo vision for navigation

The performance evaluation of a new approach to efficient disparity map computation for stereo vision is presented. Using Bayesian particle filtering to focus computational expenditure on image regions of primary importance to a navigational task allows the robust computation of a depth map sufficiently dense for most navigational tasks, while minimizing the computational load, and thus the resources, required.We show that a particleguided approach allows the efficient construction of a sparse disparity map which intelligently samples navigationally relevant scene information allowing real-time stereo computation on commodity hardware suitable for navigational tasks.