Inertial-Kinect Fusion for Robot Navigation based on the Extended Kalman Filter

Xiaoyue Sang, Zhaohui Yuan, Xiaojun Yu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The robot needs to know the pose in order to maintain stability and execute the walking path. Current solutions either rely on visual data, which is easily affected by the environment and lighting conditions, or integrate kinematics and inertial measurement unit (IMU) measurement data, however, there will be drift problems caused by accumulated errors. Aiming at the defects and stability problems of vision sensor in location, this paper combines vision sensor and IMU to complete the high-precision pose estimation at low cost, designs the combined positioning algorithm based on the extended Kalman Filter (EKF). Specifically, this paper proposes the number of correctly matched feature points and depth error as the judgment conditions of the combined strategy, and uses the IMU data to construct a process model, merges the pose estimation results of the vision sensor, and selectively corrects the vision sensor. The robot positioning experiment was carried out in the indoor laboratory scene, results show that the algorithm proposed in this paper can effectively suppress the positioning stability problem of the vision sensor and improve the accuracy of the pose estimation.

Original languageEnglish
Title of host publicationIECON 2021 - 47th Annual Conference of the IEEE Industrial Electronics Society
PublisherIEEE Computer Society
ISBN (Electronic)9781665435543
DOIs
StatePublished - 13 Oct 2021
Event47th Annual Conference of the IEEE Industrial Electronics Society, IECON 2021 - Toronto, Canada
Duration: 13 Oct 202116 Oct 2021

Publication series

NameIECON Proceedings (Industrial Electronics Conference)
Volume2021-October

Conference

Conference47th Annual Conference of the IEEE Industrial Electronics Society, IECON 2021
Country/TerritoryCanada
CityToronto
Period13/10/2116/10/21

Keywords

  • combined positioning
  • Extended Kalman filter
  • IMU
  • robot vision

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