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| Content Provider | Springer Nature Link |
|---|---|
| Author | Hollinger, Geoffrey A. Djugash, Joseph Singh, Sanjiv |
| Copyright Year | 2011 |
| Abstract | We propose a framework for utilizing fixed ultra-wideband ranging radio nodes to track a moving target radio node in an environment without guaranteed line of sight or accurate odometry. For the case where the fixed nodes’ locations are known, we derive a Bayesian room-level tracking method that takes advantage of the structural characteristics of the environment to ensure robustness to noise. For the case of unknown fixed node locations, we present a two-step approach that first reconstructs the target node’s path using Gaussian Process Latent Variable models (GPLVMs) and then uses that path to determine the locations of the fixed nodes. We present experiments verifying our algorithm in an office environment, and we compare our results to those generated by online and batch SLAM methods, as well as odometry mapping. Our algorithm is successful at tracking a moving target node without odometry and mapping the locations of fixed nodes using radio ranging data that are both noisy and intermittent. |
| Starting Page | 1 |
| Ending Page | 14 |
| Page Count | 14 |
| File Format | |
| ISSN | 09295593 |
| Journal | Autonomous Robots |
| Volume Number | 32 |
| Issue Number | 1 |
| e-ISSN | 15737527 |
| Language | English |
| Publisher | Springer US |
| Publisher Date | 2011-07-12 |
| Publisher Place | Boston |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | Range sensing Sensor networks Target tracking Artificial Intelligence (incl. Robotics) Computer Imaging, Vision, Pattern Recognition and Graphics Control, Robotics, Mechatronics Robotics and Automation |
| Content Type | Text |
| Resource Type | Article |
| Subject | Artificial Intelligence |
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