Name
Abstract | ||
|---|---|---|
This paper describes a localisation framework that combines the accuracy of feature maps with the scalability of topological maps. The map is structured as a graph of nodes where each node defines a local region feature map. This breaks the localisation process into a combination of regional feature tracking and node-to-node context switching. As part of the practical implementation of the localisation system, we introduce a batch data association method that uses the simultaneous observation of multiple features to determine data associations in a manner decoupled from the vehicle pose estimate. We also present an observation-based dead reckoning procedure that estimates vehicle motion in place of odometry and does not require a kinematic vehicle model. Experimental results demonstrate that this approach is capable of localising in large-scale outdoor environments. We perform tests in an inner city park and a suburban street using a scanning range laser as the sole information source. The diverse nature of these two environments indicates that these techniques have broad application. |
Year | DOI | Venue |
|---|---|---|
2001 | 10.1016/S0921-8890(01)00163-4 | Robotics and Autonomous Systems |
Keywords | Field | DocType |
Outdoor navigation,Autonomous systems,Data association,Topological feature maps,Odometry-free dead reckoning | Computer vision,Kinematics,Computer science,Simulation,Odometry,Data association,Dead reckoning,Autonomous system (Internet),Artificial intelligence,Feature tracking,Scalability,Context switch | Journal |
Volume | Issue | ISSN |
37 | 4 | 0921-8890 |
Citations | PageRank | References |
17 | 1.96 | 20 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tim Bailey | 1 | 1440 | 85.67 |
| Eduardo Nebot | 2 | 126 | 11.54 |