Gamma-SLAM: Using stereo vision and variance grid maps for SLAM in unstructured environments

We introduce a new method for stereo visual SLAM (simultaneous localization and mapping) that works in unstructured, outdoor environments. 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. This idea was motivated by our experience with outdoor navigation tasks, which has shown height variance to be a useful measure of traversability. 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. Visual odometry provides good proposal distributions for the particle pose. 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 verify the algorithm's accuracy on two outdoor courses by comparing with ground truth data obtained using electronic surveying equipment. In addition, we solve for the optimal transformation from the SLAM map to georeferenced coordinates, based on a noisy GPS signal. We derive an online version of this alignment process, which can be used to maintain a running estimate of the robot's global position that is much more accurate than the GPS readings.