Camera-Based Belief Space Planning in Discrete Partially-Observable Domains

Robots often have to operate in discrete partially observable worlds, where the state of the world is only observ- able at runtime. To react to different world states, robots need contingencies. To find contingencies, prior work developed the path tree optimization (PTO) method, which computes motion contingencies by constructing a tree of motion paths in belief space. In this paper, we extend upon PTO by enabling camera- based belief space planning through an extension of the open motion planning library (OMPL). By leveraging this extension, we develop an improved camera-based state sampler and an efficient open-source implementation of PTO. This version of PTO supports a virtual camera, non-euclidean state spaces, and different state samplers. We evaluate this improved version of PTO on four realistic scenarios with a virtual camera in up to 10-dimensional state spaces. In our evaluations, we compare PTO both with a default and with the new camera-based state sampler. The results indicate that the camera-based state sampler improves success rates in 3 out of 4 scenarios while having a significant lower memory footprint. Our work thus makes an important step in advancing belief-space planning and provides researchers with an open source tool to use, modify, and benchmark belief-space planning methods.