Robot Localization Based on Planned Path Constraints

HU Zhaozheng; XU Cong; ZHOU Zhe; DENG Zewu

doi:10.11999/JEIT210984

Volume 44 Issue 11

Nov. 2022

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Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(11): 3941-3950

HU Zhaozheng, XU Cong, ZHOU Zhe, DENG Zewu. Robot Localization Based on Planned Path Constraints[J]. Journal of Electronics & Information Technology, 2022, 44(11): 3941-3950. doi: 10.11999/JEIT210984

Citation:

HU Zhaozheng, XU Cong, ZHOU Zhe, DENG Zewu. Robot Localization Based on Planned Path Constraints[J]. Journal of Electronics & Information Technology, 2022, 44(11): 3941-3950. doi: 10.11999/JEIT210984

HU Zhaozheng, XU Cong, ZHOU Zhe, DENG Zewu. Robot Localization Based on Planned Path Constraints[J]. Journal of Electronics & Information Technology, 2022, 44(11): 3941-3950. doi: 10.11999/JEIT210984

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Robot Localization Based on Planned Path Constraints

doi: 10.11999/JEIT210984

HU Zhaozheng^{1, 2
,
,},
XU Cong^{1, 2},
ZHOU Zhe^{2, 3},
DENG Zewu^{1, 2}

1.
School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China
2.
Intelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan 430063, China
3.
Chongqing Research Institute, Wuhan University of Technology, Chongqing 401120, China

Funds: The National Natural Science Foundation of China(U1764262), The Enterprise Technology Innovation Project of Wuhan Science and Technology Bureau(2020010601012165, 2020010602011973, 2020010602012003), The Scientific and Technological Innovation Research and Development Project of Chongqing Research Institute of Wuhan University of Technology(YF2021-04)

Received Date: 2021-09-15
Rev Recd Date: 2022-04-07

Available Online: 2022-04-22

Publish Date: 2022-11-14

Abstract

Abstract

Path planning is a step to generate a feasible path for a robot to track along. Locations of the robot are supposed to lie on or at least nearby the planned path, which can thus generate important constraints for robot localization. In this paper, a model, called Path-Induced Location Probability Map (PI-LPM), to exploit such constraint on robot localization is proposed. The proposed PI-LPM model is a Probability Density Function (PDF) over the entire map with the probability to describe the likelihood that the robot is located. The PDF is generated from all the points representing the path by applying the Kernel Density Estimation (KDE) method with each point as a sampling point. Based on the PI-LPM model, a Robot Localization from Planned Path Constraints (RL-PPC) method to enhance robot localization is proposed. In this method, particle filter is applied to fuse the develop PI-LPM model and existing localization methods, where the probability from PI-LPM is an important factor to assign weights to the particles. The proposed method is validated with both simulation and real data. In the experiment, the proposed PI-LPM model is integrated into both GPS and LiDAR based localization systems. Experimental results demonstrate that the RL-PPC method can effectively improve the over-all performance of robot localization.
- Robot localization,
- Probability map model,
- Path planning,
- Kernel Density Estimation (KDE),
- Particle filter

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References(18)

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