A Spatially and Temporally Scalable Approach for Long-Term Lakeshore Monitoring.

This paper provides an image processing framework to assist in the inspection and, more generally, the data association of a natural environment, which we demonstrate in a long-term lakeshore monitoring task with an autonomous surface vessel. Our domain consists of 55 surveys of a 1 km lakeshore collected over a year and a half. Our previous work introduced a framework in which images of the same scene from different surveys are aligned using visual SLAM and SIFT Flow. This paper: (1) minimizes the number of expensive image alignments between two surveys using a covering set of poses, rather than all the poses in a sequence; (2) improves alignment quality using a local search around each pose and an alignment bias derived from the 3D information from visual SLAM; and (3) provides exhaustive results of image alignment quality. Our improved framework finds significantly more precise alignments despite performing image registration over an order of magnitude fewer times. We show changes a human spotted between surveys that would have otherwise gone unnoticed. We also show cases where our approach was robust to 'extreme' variation in appearance.