For the last three years, we have developed and researched multiple collaborative robots, CoBots, which have been autonomously traversing our multi-floor buildings. We pursue the goal of long-term autonomy for indoor service mobile robots as the ability for them to be deployed indefinitely while they perform tasks in an evolving environment. The CoBots include several levels of autonomy, and in this paper we focus on their localization and navigation algorithms. We present the Corrective Gradient Refinement (CGR) algorithm, which refines the proposal distribution of the particle filter used for localization with sensor observations using analytically computed state space derivatives on a vector map. We also present the Fast Sampling Plane Filtering algorithm that extracts planar regions from depth images in real time. These planar regions are then projected onto the 2D vector map of the building, and along with the laser rangefinder observations, used with CGR for localization. For navigation, we present a hierarchical planner, which computes a topological policy using a graph representation of the environment, computes motion commands based on the topological policy, and then modifies the motion commands to side-step perceived obstacles. We started logging the deployments of the CoBots one and a half years ago, and have since collected logs of the CoBots traversing more than 130 km over 1082 deployments and a total run time of 182 h, which we publish as a dataset consisting of more than 10 million laser scans. The logs show that although there have been continuous changes in the environment, the robots are robust to most of them, and there exist only a few locations where changes in the environment cause increased uncertainty in localization.
Available at: http://works.bepress.com/joydeep-biswas/8/