Information-Efficient 3-D Visual SLAM for Unstructured Domains

This paper presents a novel vision-based sensory package and an information-efficient simultaneous localization and mapping (SLAM) algorithm. Together, we offer a solution for building 3-D dense map in an unknown and unstructured environment with minimal computational costs. The sensory package we adopt consists of a conventional camera and a range imager, which provide range and bearing and elevation inputs as commonly used by 3-D feature-based SLAM. In addition, we propose an algorithm to give the robots the `intelligencerdquo to select, out of the steadily collected data, the maximally informative observations to be used in the estimation process. We show that, although the actual evaluation of information gain for each frame introduces an additional computational cost, the overall efficiency is significantly increased by keeping the matrix compact. The noticeable advantage of this strategy is that the continuously gathered data are not heuristically segmented prior to being input to the filter. Quite the opposite, the scheme lends itself to be statistically optimal and is capable of handling large datasets collected at realistic sampling rates.