Abstract-In graph-based SLAM, the pose graph encodes the poses of the robot during data acquisition as well as spatial constraints between them. The size of the pose graph has a substantial influence on the runtime and the memory requirements of a SLAM system, which hinders long-term mapping. In this paper, we address the problem of efficient information-theoretic compression of pose graphs. Our approach estimates the expected information gain of laser measurements with respect to the resulting occupancy grid map. It allows for restricting the size of the pose graph depending on the information that the robot acquires about the environment or based on a given memory limit, which results in an any-space SLAM system. When discarding laser scans, our approach marginalizes out the corresponding pose nodes from the graph. To avoid a densely connected pose graph, which would result from exact marginalization, we propose an approximation to marginalization that is based on local Chow-Liu trees and maintains a sparse graph. Real world experiments suggest that our approach effectively reduces the growth of the pose graph while minimizing the loss of information in the resulting grid map.

A large number of applications use motion capture systems to track the location and the body posture of people. For instance, the movie industry captures actors to animate virtual characters that perform stunts. Today's tracking systems either operate with statically mounted cameras and thus can be used in confined areas only or rely on inertial sensors that allow for free and large-scale motion but suffer from drift in the pose estimate. This paper presents a novel tracking approach that aims to provide globally aligned full body posture estimates by combining a mobile robot and an inertial motion capture system. In our approach, a mobile robot equipped with a laser scanner is used to anchor the pose estimates of a person given a map of the environment. It uses a particle filter to globally localize a person wearing a motion capture suit and to robustly track the person's position. To obtain a smooth and globally aligned trajectory of the person, we solve a least squares optimization problem formulated from the motion capture suite and tracking data. Our approach has been implemented on a real robot and exhaustively tested. As the experimental evaluation shows, our system is able to provide locally precise and globally aligned estimates of the person's full body posture.