| Autor: |
Ahmad Pishehvari, Bernd Tibken, Uri Iurgel, Markus Stefer |
| Rok vydání: |
2020 |
| Předmět: |
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| Zdroj: |
2020 5th International Conference on Control and Robotics Engineering (ICCRE). |
| DOI: |
10.1109/iccre49379.2020.9096494 |
| Popis: |
This paper presents a novel local self-localization approach which formulates the registration problem as a range-Doppler response problem. The proposed registration algorithm utilizes sensor observations in range-Doppler domain in combination with a pre-built high precision map and motion sensors for velocity and yaw rate measurement. In contrast to state-of-the-art approaches which perform the registration based on a point cloud, the introduced algorithm in this work shifts the registration from detection domain down to the sensor range-Doppler response. This procedure offers several positive characteristics such as independence of targets angle estimation, registration without any necessity of further processing of range-Doppler response for extracting detections and generating a point cloud. Furthermore, the transformation of the extracted detection from range-Doppler to Cartesian domain is not required.The registration problem is constructed as an optimization-based approach. The map information is converted to the range-Doppler domain and the optimization problem searches for the best overlap between sensor observation and map information. The registration result is subsequently used for correcting the ego-pose which is initially estimated based on motion sensors. The extensive simulation results demonstrate the feasibility and precise performance of the proposed algorithm. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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