An evidence-based approach to object pose estimation from LiDAR measurements in challenging environments
| Autor: | Tyson Govan Phillips, Peter Ross McAree |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
0209 industrial biotechnology
business.industry Computer science 02 engineering and technology Object (computer science) Automation Field (computer science) Computer Science Applications Excavator 020901 industrial engineering & automation Lidar Control and Systems Engineering 0202 electrical engineering electronic engineering information engineering Range (statistics) 020201 artificial intelligence & image processing Segmentation Computer vision Artificial intelligence business Pose |
| Zdroj: | Journal of Field Robotics. 35:921-936 |
| ISSN: | 1556-4959 |
| Popis: | This paper addresses the problem of estimating object pose from high-density LiDAR measurements in unpredictable field robotic environments. Point-cloud measurements collected in such environments do not lend themselves to providing an initial estimate or systematic segmentation of the point-cloud. A novel approach is presented that evaluates measurements individually for the evidence they provide to a collection of pose hypotheses. A maximum evidence strategy is constructed that is based in the idea that the most likely pose must be that which is most consistent with the observed LiDAR range measurements. This evidence-based approach is shown to handle the diversity of range measurements without an initial estimate or segmentation. The method is robust to dust. The approach is demonstrated by two pose estimation problems associated with the automation of a large mining excavator. |
| Databáze: | OpenAIRE |
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