  Humans are remarkable at navigating and moving through dynamic and complex
spaces, such as crowded streets. For robots to do the same, it is crucial that
they are endowed with highly reactive obstacle avoidance robust to partial and
poor sensing. We address the issue of enabling obstacle avoidance based on
sparse and asynchronous perception. The proposed control scheme combines a
high-level input command provided by either a planner or a human operator with
fast reactive obstacle avoidance. The sampling-based sensor data can be
combined with an analytical reconstruction of the obstacles for real-time
collision avoidance. We can ensure that the agent does not get stuck when a
feasible path exists between obstacles. The algorithm was evaluated
experimentally on static laser data from cluttered, indoor office environments.
Additionally, it was used in a shared control mode in a dynamic and complex
outdoor environment in the center of Lausanne. The proposed control scheme
successfully avoided collisions in both scenarios. During the experiments, the
controller on the onboard computer took 1 millisecond to evaluate over 30000
data points.
