상세정보
학술지
Spherically Stratified Point Projection: Feature Image Generation for Object Classification Using 3D LiDAR Data
- 발행일
- 202112
- 출처
- Sensors, v.21 no.23, pp.1-14
- ISSN
- 1424-8220
- 출판사
- MDPI
- 협약과제
- 20HS1400, 노동환경 개선을 위한 로봇 작업지능 핵심기술 개발, 유원필
- 초록
- Three-dimensional point clouds have been utilized and studied for the classification of objects at the environmental level. While most existing studies, such as those in the field of computer vision, have detected object type from the perspective of sensors, this study developed a specialized strategy for object classification using LiDAR data points on the surface of the object. We propose a method for generating a spherically stratified point projection (sP2 ) feature image that can be applied to existing image-classification networks by performing pointwise classification based on a 3D point cloud using only LiDAR sensors data. The sP2 's main engine performs image generation through spherical stratification, evidence collection, and channel integration. Spherical stratification categorizes neighboring points into three layers according to distance ranges. Evidence collection calculates the occupancy probability based on Bayes?? rule to project 3D points onto a two-dimensional surface corresponding to each stratified layer. Channel integration generates sP2 RGB images with three evidence values representing short, medium, and long distances. Finally, the sP2 images are used as a trainable source for classifying the points into predefined semantic labels. Experimental results indicated the effectiveness of the proposed sP2 in classifying feature images generated using the LeNet architecture.
- KSP 제안 키워드
- 3D LIDAR data, 3D point cloud, Computer Vision(CV), Evidence collection, Feature images, Image generation, LiDAR Sensor, Main engine, Neighboring points, Object classification, Occupancy probability
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