Outdoor obstacle avoidance based on hybrid visual stereo SLAM for an autonomous quadrotor MAV

摘要

We address the problem of on-line volumetric map creation of unknown environments and planning of safe trajectories. The sensor used for this purpose is a stereo camera. Our system is designed to work in GPS denied areas. We design a keyframe based hybrid SLAM algorithm which combines feature-based stereo SLAM and direct stereo SLAM. We use it to grow the map while keeping track of the camera pose in the map. The SLAM system builds a sparse map. For the path planning we build a dense volumetric map by computing dense stereo matching at keyframes and inserting the point clouds in an Octomap. The computed disparity maps are reused on the direct tracking refinement step of our hybrid SLAM. Safe trajectories are then estimated using the RRT* algorithm in the SE(3) state space. In our experiments, we show that we can map large environments with hundreds of keyframes. We also conducted autonomous outdoor flights using a quadcopter to validate our approach for obstacle avoidance.