Configuration space based efficient view planning and exploration with occupancy grids

The concept of C-space entropy for sensor-based exploration and view planning for general robot-sensor systems has been introduced in [?], [?], [?], [?]. The robot plans the next sensing action (also called the next best view) to maximize the expected C-space entropy reduction, (known as Maximal expected Entropy Reduction, or MER). It gives priority to those areas that increase the maneuverable space around the robot, taking into account its physical size and shape, thereby facilitating reachability for further views. However, previous work had assumed a Poisson point process model for obstacle distribution in the physical space, a simplifying assumption. In this paper we derive an expression for MER criterion assuming an occupancy grid map, a commonly used representation for workspace representation in much of the mobile robot community. This model is easily obtained from typical range sensors such as laser range finders, stereo vision, etc., and furthermore, we can incorporate occlusion constraints and their effect in the MER formulation, making it more realistic. Simulations show that even for holonomic mobile robots with relatively simple geometric shapes (such as a rectangle), the MER criterion yields improvement in exploration efficiency (number of views needed to explore the C-space) over physical space based criteria.