Introduction
Related research
Fast 3D localization using NDT and a particle filter
Spatial change detection using ND voxels
Classification of point distributions in ND voxels
Overlapping of voxels in map data
Voting of spatial change detection through sequential measurements
Experiments in indoor and outdoor environments
Indoor experiments
Spatial change detection in a corridor
Localization | 18.04 [s] |
Spatial change detection | 0.103 [s] |
(a) XOR | 296 |
(b) Classification | 328 |
(c) Classification and overlapping | 691 |
(d) Classification, overlapping, and voting (proposed) | 84 |
Spatial change detection in a hall
Object (size) | Detection rate [%] | ||
---|---|---|---|
Proposed | 3D-NDT [10] | \(L_2\) [18] | |
A (\(400 \times 400\) mm) | 100 | 100 | 95 |
B (\(300 \times 300\) mm) | 100 | 100 | 95 |
C (\(200 \times 200\) mm) | 85 | 75 | 50 |
D (\(100 \times 100\) mm) | 50 | 15 | 0 |
Outdoor experiments
Conclusions
- We proposed a real-time spatial change detection technique using 3D-NDT and voxel classification. The proposed technique can be integrated with the real-time localization technique [27] using 3D-NDT and a particle filter and reduce the calculation cost.
- We implemented the proposed technique using PCL library and conducted the experiments in indoor and outdoor environments using the RGB-D camera (Microsoft Kinect) and the omni-directional laser scanner (Velodyne HDL-32e).
- Through the experiments in indoor and outdoor environments, we confirmed the proposed localization and spatial change detection techniques can be processed in real-time using on-board sensors and the performance of the proposed techniques outperforms other cutting-edge techniques.