Localization and tracking in known large environments using portable real-time 3D sensors

Abstract

Ego-motion estimation and localization in large environments are key components in any assistive technology for real-time user orientation and navigation. We consider the case where a large known environment is explored without a priori assumptions on the initial location. In particular we propose a framework that uses a single portable 3D sensor to solve the place recognition problem and continuously tracks its position even when leaving the known area or when significant changes occur in the observed environment.We cast the place recognition step as a classification problem and propose an efficient search space reduction considering only navigable areas where the user can be localized. Classification hypotheses are then discarded exploiting temporal consistency w.r.t. a relative tracker that exploits only the sensor input data. The solution uses a compact classifier whose representation scales well with the map size. After being localized, the user is continuously tracked exploiting the known environment using an efficient data structure that provides constant access time for nearest neighbor searches and that can be streamed to keep only the local region close to the last known position in memory. Robust results are achieved by performing a geometrically stable selection of points, efficiently filtering outliers and integrating the relative tracker based on previous observations.We experimentally show that such a framework provides good localization results and that it scales well with the environment map size yielding real-time performance for both place recognition and tracking.