Name
Abstract | ||
|---|---|---|
Topological/metric route following, also called teach and repeat (T&R), enables long-range autonomous navigation even without globally consistent localization. In the teach pass, the robot is driven manually and builds up a topological/metric map of the environment, a graph of metric submaps connected by relative transformations. For repeating the route autonomously, the map only needs to be locally consistent; errors on the global level due to localization drift are irrelevant. This renders T&R ideal for applications in which a global positioning system may not be available, such as navigation through street canyons or forests in search and rescue, reconnaissance in underground structures, surveillance, or planetary exploration. We present a T&R system based on iterative closest point matching (ICP) using data from a spinning three-dimensional (3D) laser scanner. Our algorithm is highly accurate, robust to dynamic scenes and extreme changes in the environment, and independent of ambient lighting. It enables autonomous navigation along a taught path in both structured and unstructured environments, including highly 3D terrain. Furthermore, our system is able to detect obstacles and avoid them by adapting its path using a local motion planner. It enables autonomous route following in nonstatic environments, which is not possible with classical T&R systems. We demonstrate our algorithm's performance in two long-range driving experiments, one in a highly dynamic urban environment, the other in unstructured, rough, 3D terrain. In these experiments, our robot autonomously drove a distance of over 22km in both day and night. We analyze the localization accuracy of our system and show that it is highly precise. Moreover, we compare our ICP-based method to a state-of-the-art stereo-vision-based technique and show that our approach has a greatly increased robustness to path deviations and is less dependent on environmental conditions. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1002/rob.21524 | JOURNAL OF FIELD ROBOTICS |
Field | DocType | Volume |
Computer vision,Laser scanning,Search and rescue,Simulation,Urban environment,Robustness (computer science),Global Positioning System,Artificial intelligence,Invariant (mathematics),Engineering,Robot,Iterative closest point | Journal | 32.0 |
Issue | ISSN | Citations |
SP4.0 | 1556-4959 | 11 |
PageRank | References | Authors |
0.62 | 32 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Philipp Krüsi | 1 | 22 | 1.58 |
| Bastian Bücheler | 2 | 11 | 0.62 |
| François Pomerleau | 3 | 410 | 26.35 |
| Ulrich Schwesinger | 4 | 33 | 3.16 |
| Roland Siegwart | 5 | 7640 | 551.49 |
| Paul Timothy Furgale | 6 | 869 | 42.35 |