A Next Best View estimate may guide processes of 3D reconstruction and exploration to completeness within reasonable time. For the evaluation to be useful, the Next Best View computation itself must be effective in terms of time and accuracy. It needs to be model-free to hold for any geometry of the 3D scene. In this work, we compare the effectiveness of different approaches to Next Best View evaluation. A 3D occupancy grid map, allowing for fast lookup and ray casting, serves as a foundation for our evaluation. We tested naive, state-of-the-art and novel algorithms on data acquired from both indoor and outdoor environments. We demonstrate that the most effective volumetric algorithm is a novel one that exploits spatial hierarchy, utilizes frontiers, and avoids redundant ray casting.

Motor control depends on sensory feedback in multiple modalities with different latencies. In this paper we consider within the framework of re- inforcement learning how different sensory modalities can be combined and selected for real-time, optimal movement control. We propose an actor-critic architecture with multiple modules, whose output are com- bined using a softmax function. We tested our architecture in a simu- lation of a sequential reaching task. Reaching was initially guided by visual feedback with a long latency. Our learning scheme allowed the agent to utilize the somatosensory feedback with shorter latency when the hand is near the experienced trajectory. In simulations with different latencies for visual and somatosensory feedback, we found that the agent depended more on feedback with shorter latency.