ClusterMap Building and Relocalization in Urban Environments for Unmanned Vehicles
Abstract
:1. Introduction
2. Related Work
3. ClusterMap Building
3.1. SLAM for ClusterMap Building
3.2. Building ClusterMap
Algorithm 1 Cluster Registration. |
Require::Set of registered clusters Require::Cluster waiting for registration Require::Three clusters closest to in 1: for each do 2: if sqrDist()>maxDist then 3: ; break; 4: end if 5: if sqrDist()<minDist then 6: ; 7: else 8: ; 9: for all do 10: if radiusSearch(,,rad)>minNum then 11: ; 12: end if 13: end for 14: if >sizeof() / thresholdNum then 15: ; 16: end if 17: end if 18: end for |
3.3. Cluster Descriptor for Clusters in ClusterMap
4. Relocalization Algorithm Based on ClusterMap
4.1. Cluster Descriptor Matching
4.2. Removing Outliers Based on Geometric Verification
- Length condition: Use distances between clusters included in to filter out some unsatisfied candidates. In any other set, , a cluster, , should be found so that
- Inclusion condition: Let be the maximum distance between and all other clusters in the local ClusterMap . Therefore, clusters are present in the circle, with as the center and as the radius. Correspondingly, in the global ClusterMap, ~ clusters are available in the circle, with as the center and as the radius. The cluster is preserved only if enough different groups exist in this circular range.
- Triangular condition: A cluster and every two other clusters in can form a base triangle (the blue dotted triangle shown in Figure 7c); if clusters in the corresponding groups can form a triangle similar to the base one, then the cluster is retained. By randomly selecting two clusters from except , denoted as and , and a should be derived from and , respectively, satisfying
5. Experiments
5.1. Evaluation on KITTI Data Set
5.2. Evaluation with Our Experimental Vehicle
5.3. Parameters Evaluation
5.4. Discussion
6. Conclusions
Supplementary Materials
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Abbreviations
SLAM | Simultaneous Localization and Mapping |
PFHs | Point Feature Histograms |
FPFHs | Fast Point Feature Histograms |
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Pan, Z.; Chen, H.; Li, S.; Liu, Y. ClusterMap Building and Relocalization in Urban Environments for Unmanned Vehicles. Sensors 2019, 19, 4252. https://doi.org/10.3390/s19194252
Pan Z, Chen H, Li S, Liu Y. ClusterMap Building and Relocalization in Urban Environments for Unmanned Vehicles. Sensors. 2019; 19(19):4252. https://doi.org/10.3390/s19194252
Chicago/Turabian StylePan, Zhichen, Haoyao Chen, Silin Li, and Yunhui Liu. 2019. "ClusterMap Building and Relocalization in Urban Environments for Unmanned Vehicles" Sensors 19, no. 19: 4252. https://doi.org/10.3390/s19194252