An improved method for odometry estimation based on EKF and Temporal Convolutional Network

Xiaokun Leng; Songhao Piao; Song Wang; Lin Chang; Zheng Zhu

doi:10.1016/j.phycom.2020.101178

An improved method for odometry estimation based on EKF and Temporal Convolutional Network

Xiaokun Leng
, Songhao Piao^*
, Song Wang
, Lin Chang
, Zheng Zhu

^*Corresponding author for this work

School of Computer Science and Technology, Harbin Institute of Technology
Leju (Shenzhen) Robotics

Research output: Contribution to journal › Article › peer-review

3 Scopus citations

Abstract

Compared with wheeled robots, the walking direction and distance of biped robots are difficult to accurately control, and the body of the robot will periodically shake during walking. During the walking of the biped robot, the movement of the torso drives the head camera to shake, which causes great interference to the slam algorithm, and leads to deviations in the vision-based mileage estimation method. This paper mainly combines the vision positioning method and the sensor data on the biped robot and estimates the position information of the robot through data fusion. This method makes full use of the sensor data on the robot to improve the estimation accuracy of the odometer.

Original language	English
Article number	101178
Journal	Physical Communication
Volume	43
DOIs	https://doi.org/10.1016/j.phycom.2020.101178
State	Published - Dec 2020
Externally published	Yes

Keywords

Extended Kalman filter
Humandoid robot
Neural network
Sensor fusion

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10.1016/j.phycom.2020.101178

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@article{c7caa7d9b0994f74a6b544a5a84d494b,

title = "An improved method for odometry estimation based on EKF and Temporal Convolutional Network",

abstract = "Compared with wheeled robots, the walking direction and distance of biped robots are difficult to accurately control, and the body of the robot will periodically shake during walking. During the walking of the biped robot, the movement of the torso drives the head camera to shake, which causes great interference to the slam algorithm, and leads to deviations in the vision-based mileage estimation method. This paper mainly combines the vision positioning method and the sensor data on the biped robot and estimates the position information of the robot through data fusion. This method makes full use of the sensor data on the robot to improve the estimation accuracy of the odometer.",

keywords = "Extended Kalman filter, Humandoid robot, Neural network, Sensor fusion",

author = "Xiaokun Leng and Songhao Piao and Song Wang and Lin Chang and Zheng Zhu",

note = "Publisher Copyright: {\textcopyright} 2020 Elsevier B.V.",

year = "2020",

month = dec,

doi = "10.1016/j.phycom.2020.101178",

language = "英语",

volume = "43",

journal = "Physical Communication",

issn = "1874-4907",

publisher = "Elsevier B.V.",

}

TY - JOUR

T1 - An improved method for odometry estimation based on EKF and Temporal Convolutional Network

AU - Leng, Xiaokun

AU - Piao, Songhao

AU - Wang, Song

AU - Chang, Lin

AU - Zhu, Zheng

PY - 2020/12

Y1 - 2020/12

N2 - Compared with wheeled robots, the walking direction and distance of biped robots are difficult to accurately control, and the body of the robot will periodically shake during walking. During the walking of the biped robot, the movement of the torso drives the head camera to shake, which causes great interference to the slam algorithm, and leads to deviations in the vision-based mileage estimation method. This paper mainly combines the vision positioning method and the sensor data on the biped robot and estimates the position information of the robot through data fusion. This method makes full use of the sensor data on the robot to improve the estimation accuracy of the odometer.

AB - Compared with wheeled robots, the walking direction and distance of biped robots are difficult to accurately control, and the body of the robot will periodically shake during walking. During the walking of the biped robot, the movement of the torso drives the head camera to shake, which causes great interference to the slam algorithm, and leads to deviations in the vision-based mileage estimation method. This paper mainly combines the vision positioning method and the sensor data on the biped robot and estimates the position information of the robot through data fusion. This method makes full use of the sensor data on the robot to improve the estimation accuracy of the odometer.

KW - Extended Kalman filter

KW - Humandoid robot

KW - Neural network

KW - Sensor fusion

UR - https://www.scopus.com/pages/publications/85089430011

U2 - 10.1016/j.phycom.2020.101178

DO - 10.1016/j.phycom.2020.101178

M3 - 文章

AN - SCOPUS:85089430011

SN - 1874-4907

VL - 43

JO - Physical Communication

JF - Physical Communication

M1 - 101178

ER -

An improved method for odometry estimation based on EKF and Temporal Convolutional Network

Abstract

Keywords

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