Indoor Global Localization Using Depth-Guided Photometric Edge Descriptor for Mobile Robot Navigation
| Autor: | Howon Cheong, Euntai Kim, Sung-Kee Park |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Robot kinematics
Landmark Matching (graph theory) Computer science business.industry Feature extraction ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Sensor fusion Mobile robot navigation Robot Computer vision Artificial intelligence Electrical and Electronic Engineering business Instrumentation Pose |
| Zdroj: | IEEE Sensors Journal. 19:10837-10847 |
| ISSN: | 2379-9153 1530-437X |
| DOI: | 10.1109/jsen.2019.2932131 |
| Popis: | This paper suggests a new landmark descriptor for indoor mobile robot navigation with sensor fusion and a global localization method using it. In previous research on robot pose estimation, various landmarks such as geometric features, visual local-invariant features, or objects are utilized. However, in real-world situations, there is a possibility that distinctive landmarks are insufficient or there are many similar landmarks repeated in indoor environment, which makes accurate pose estimation difficult. In this work, we suggest a new landmark descriptor, called depth-guided photometric edge descriptor (DPED), which is composed of superpixels and approximated 3D depth information of photometric vertical edge. With this descriptor, we propose a global localization method based on coarse-to-fine strategy. In the coarse step, candidate nodes are found by place recognition using our pairwise constraint-based spectral matching technique, and the robot pose is estimated with a probabilistic scan matching in the fine step. The experimental results show that our method successfully estimates the robot pose in the real-world tests even when there is a lack of distinctive features and objects. |
| Databáze: | OpenAIRE |
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