{"title":"INS/MPS/LiDAR Integrated Navigation System Using Federated Kalman Filter in an Indoor Environment","authors":"Taehoon Lee, Byungjin Lee, Jae-Ryong Yun, S. Sung","doi":"10.1109/PLANS53410.2023.10140065","DOIUrl":null,"url":null,"abstract":"In this paper, we propose a method to integrate data from Inertial Navigation System (INS), Magnetic Pose Estimation System (MPS), and Laser Imaging Detection and Ranging (LiDAR) using a Federated Kalman Filter (FKF). We adaptively adjusted the information sharing factor using the Mahalanobis distance to maintain navigation performance in indoor environments with mirrors that contaminate LiDAR measurements. By adaptively adjusting the information sharing factor, we can adjust the weight of each local filter. To validate navigation performance, we conducted UGV driving tests in various indoor environments. We conducted experiments by driving a UGV on a course with a diameter of 3.6 meters. UGVs are equipped with LiDAR, MPS receivers, and IMUs to measure data. We used four 1-meter diameter MPS coils. An optical motion capture device, the Optitrack, was used as reference data.","PeriodicalId":344794,"journal":{"name":"2023 IEEE/ION Position, Location and Navigation Symposium (PLANS)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 IEEE/ION Position, Location and Navigation Symposium (PLANS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/PLANS53410.2023.10140065","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
In this paper, we propose a method to integrate data from Inertial Navigation System (INS), Magnetic Pose Estimation System (MPS), and Laser Imaging Detection and Ranging (LiDAR) using a Federated Kalman Filter (FKF). We adaptively adjusted the information sharing factor using the Mahalanobis distance to maintain navigation performance in indoor environments with mirrors that contaminate LiDAR measurements. By adaptively adjusting the information sharing factor, we can adjust the weight of each local filter. To validate navigation performance, we conducted UGV driving tests in various indoor environments. We conducted experiments by driving a UGV on a course with a diameter of 3.6 meters. UGVs are equipped with LiDAR, MPS receivers, and IMUs to measure data. We used four 1-meter diameter MPS coils. An optical motion capture device, the Optitrack, was used as reference data.