Sciweavers

Share
KI
2010
Springer

Lifelong Map Learning for Graph-based SLAM in Static Environments

10 years 6 months ago
Lifelong Map Learning for Graph-based SLAM in Static Environments
In this paper, we address the problem of lifelong map learning in static environments with mobile robots using the graph-based formulation of the simultaneous localization and mapping problem. The pose graph, which stores the poses of the robot and spatial constraints between them, is the central data structure in graph-based SLAM. The size of the pose graph has a direct influence on the runtime and the memory complexity of the SLAM system and typically grows over time. A robot that performs lifelong mapping in a bounded environment has to limit the memory and computational complexity of its mapping system. We present a novel approach to prune the pose graph so that it only grows when the robot acquires relevant new information about the environment in terms of expected information gain. As a result, our approach scales with the size of the environment and not with the length of the trajectory, which is an important prerequisite for lifelong map learning. The experiments presented in t...
Henrik Kretzschmar, Giorgio Grisetti, Cyrill Stach
Added 20 May 2011
Updated 20 May 2011
Type Journal
Year 2010
Where KI
Authors Henrik Kretzschmar, Giorgio Grisetti, Cyrill Stachniss
Comments (0)
books