Information-Theoretic Planning with Trajectory Optimization for Dense 3D Mapping
Benjamin Charrow, Gregory Kahn, Sachin Patil, Sikang Liu, Ken Goldberg, Pieter Abbeel, Nathan Michael, Vijay Kumar · 2015
We propose an information-theoretic planning approach that enables mobile robots to autonomously construct dense 3D maps in a computationally efficient manner.Inspired by prior work, we accomplish this task by formulating an information-theoretic objective function based on Cauchy-Schwarz quadratic mutual information (CSQMI) that guides robots to obtain measurements in uncertain regions of the map.We then contribute a two stage approach for active mapping.First, we generate a candidate set of trajectories using a combination of global planning and generation of local motion primitives.From this set, we choose a trajectory that maximizes the information-theoretic objective.Second, we employ a gradientbased trajectory optimization technique to locally refine the chosen trajectory such that the CSQMI objective is maximized while satisfying the robot's motion constraints.We evaluated our approach through a series of simulations and experiments on a ground robot and an aerial robot mapping unknown 3D environments.Real-world experiments suggest our approach reduces the time to explore an environment by 70% compared to a closest frontier exploration strategy and 57% compared to an information-based strategy that uses global planning, while simulations demonstrate the approach extends to aerial robots with higher-dimensional state.