Heuristic search planning to reduce exploration uncertainty

The path followed by a mobile robot while mapping an environment (i.e. an exploration trajectory) plays a large role in determining the efficiency of the mapping process and the accuracy of any resulting metric map of the environment. This paper examines some important aspects of path planning in this context: the trade-offs between the speed of the exploration process versus the accuracy of resulting maps; and alternating between exploration of new territory and planning through known maps. The resulting motion planning strategy and associated heuristic are targeted to a robot building a map of an environment assisted by a Sensor Network composed of uncalibrated monocular cameras. An adaptive heuristic exploration strategy based on A* search over a combined distance and uncertainty cost function allows for adaptation to the environment and improvement in mapping accuracy. We assess the technique using an illustrative experiment in a real environment and a set of simulations in a parametric family of idealized environments.