Name
Abstract | ||
|---|---|---|
Dynamic vehicle tracking is an important module for Autonomous Land Vehicle (ALV) navigation in outdoor environments. The key step for a successful tracker is to accurately estimate the pose of the vehicle. In this paper, we present a novel real-time vehicle pose estimation algorithm based on the likelihood field model built on the Velodyne LIDAR data. The likelihood field model is adopted to weight the particles, which represent the potential poses, drawn around the location of the target vehicle. Importance sampling which is speeded up with the Scaling Series algorithm, is then exploited to choose the best particle as the final vehicle's pose. The performance of the algorithm is validated on the data collected by our own ALV in various urban environments. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/ITSC.2015.58 | ITSC |
Keywords | Field | DocType |
real-time vehicle pose estimation algorithm,likelihood field model,Velodyne LIDAR data,importance sampling,scaling series algorithm,autonomous land vehicle,ALV,vehicle tracking | Computer vision,Importance sampling,Simulation,Pose,Lidar,Sampling (statistics),Artificial intelligence,Lidar data,Engineering,Vehicle tracking system,Automatic vehicle location | Conference |
ISSN | Citations | PageRank |
2153-0009 | 1 | 0.35 |
References | Authors | |
9 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tongtong Chen | 1 | 61 | 6.88 |
| Bin Dai | 2 | 69 | 9.23 |
| Daxue Liu | 3 | 116 | 10.89 |
| Hao Fu | 4 | 10 | 2.51 |
| jinze song | 5 | 7 | 1.14 |
| chongyang wei | 6 | 1 | 0.35 |