Gamma-SLAM: Stereo Visual SLAM in Unstructured Environments Using Variance Grid Maps

We introduce a new method for stereo visual SLAM (simultaneous localization and mapping) that works in unstructured, outdoor environments. Observations use dense stereo vision to measure the variance of the heights in each cell of a 2D grid. Unlike other grid-based SLAM algorithms, which use occupancy grid maps, our algorithm uses a new mapping technique that maintains a posterior distribution over the height variance in each cell. To obtain a joint posterior over poses and maps, we use a Rao-Blackwellized particle filter: the pose distribution is estimated using a particle filter, and each particle has its own map that is obtained through exact filtering conditioned on the particle’s pose. For the particle filter over pose, visual odometry (VO) provides good proposal distributions. In the analytical (exact) filter for the map, we update the sufficient statistics of a gamma distribution over the precision (inverse variance) of heights in each grid cell. We demonstrate performance on two outdoor courses, and verify the accuracy of the algorithm by comparing with ground truth data obtained using electronic surveying equipment.