RTLIO: Real-Time LiDAR-Inertial Odometry and Mapping for UAVs
Most UAVs rely on GPS for localization in an outdoor environment. However, in GPS-denied environment, other sources of localization are required for UAVs to conduct feedback control and navigation. LiDAR has been used for indoor localization, but the sampling rate is usually too low for feedback con...
Main Authors: | , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2021-06-01
|
Series: | Sensors |
Subjects: | |
Online Access: | https://www.mdpi.com/1424-8220/21/12/3955 |
id |
doaj-bb76848858b649f6af304c6aa1d4bf03 |
---|---|
record_format |
Article |
spelling |
doaj-bb76848858b649f6af304c6aa1d4bf032021-06-30T23:37:40ZengMDPI AGSensors1424-82202021-06-01213955395510.3390/s21123955RTLIO: Real-Time LiDAR-Inertial Odometry and Mapping for UAVsJung-Cheng Yang0Chun-Jung Lin1Bing-Yuan You2Yin-Long Yan3Teng-Hu Cheng4Department of Mechanical Engineering, National Yang Ming Chiao Tung University, Hsinchu 30010, TaiwanDepartment of Mechanical Engineering, National Yang Ming Chiao Tung University, Hsinchu 30010, TaiwanDepartment of Mechanical Engineering, National Yang Ming Chiao Tung University, Hsinchu 30010, TaiwanDepartment of Mechanical Engineering, National Yang Ming Chiao Tung University, Hsinchu 30010, TaiwanDepartment of Mechanical Engineering, National Yang Ming Chiao Tung University, Hsinchu 30010, TaiwanMost UAVs rely on GPS for localization in an outdoor environment. However, in GPS-denied environment, other sources of localization are required for UAVs to conduct feedback control and navigation. LiDAR has been used for indoor localization, but the sampling rate is usually too low for feedback control of UAVs. To compensate this drawback, IMU sensors are usually fused to generate high-frequency odometry, with only few extra computation resources. To achieve this goal, a real-time LiDAR inertial odometer system (RTLIO) is developed in this work to generate high-precision and high-frequency odometry for the feedback control of UAVs in an indoor environment, and this is achieved by solving cost functions that consist of the LiDAR and IMU residuals. Compared to the traditional LIO approach, the initialization process of the developed RTLIO can be achieved, even when the device is stationary. To further reduce the accumulated pose errors, loop closure and pose-graph optimization are also developed in RTLIO. To demonstrate the efficacy of the developed RTLIO, experiments with long-range trajectory are conducted, and the results indicate that the RTLIO can outperform LIO with a smaller drift. Experiments with odometry benchmark dataset (i.e., KITTI) are also conducted to compare the performance with other methods, and the results show that the RTLIO can outperform ALOAM and LOAM in terms of exhibiting a smaller time delay and greater position accuracy.https://www.mdpi.com/1424-8220/21/12/3955LiDAR-inertial odometrystate estimationsensor fusionSLAM |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Jung-Cheng Yang Chun-Jung Lin Bing-Yuan You Yin-Long Yan Teng-Hu Cheng |
spellingShingle |
Jung-Cheng Yang Chun-Jung Lin Bing-Yuan You Yin-Long Yan Teng-Hu Cheng RTLIO: Real-Time LiDAR-Inertial Odometry and Mapping for UAVs Sensors LiDAR-inertial odometry state estimation sensor fusion SLAM |
author_facet |
Jung-Cheng Yang Chun-Jung Lin Bing-Yuan You Yin-Long Yan Teng-Hu Cheng |
author_sort |
Jung-Cheng Yang |
title |
RTLIO: Real-Time LiDAR-Inertial Odometry and Mapping for UAVs |
title_short |
RTLIO: Real-Time LiDAR-Inertial Odometry and Mapping for UAVs |
title_full |
RTLIO: Real-Time LiDAR-Inertial Odometry and Mapping for UAVs |
title_fullStr |
RTLIO: Real-Time LiDAR-Inertial Odometry and Mapping for UAVs |
title_full_unstemmed |
RTLIO: Real-Time LiDAR-Inertial Odometry and Mapping for UAVs |
title_sort |
rtlio: real-time lidar-inertial odometry and mapping for uavs |
publisher |
MDPI AG |
series |
Sensors |
issn |
1424-8220 |
publishDate |
2021-06-01 |
description |
Most UAVs rely on GPS for localization in an outdoor environment. However, in GPS-denied environment, other sources of localization are required for UAVs to conduct feedback control and navigation. LiDAR has been used for indoor localization, but the sampling rate is usually too low for feedback control of UAVs. To compensate this drawback, IMU sensors are usually fused to generate high-frequency odometry, with only few extra computation resources. To achieve this goal, a real-time LiDAR inertial odometer system (RTLIO) is developed in this work to generate high-precision and high-frequency odometry for the feedback control of UAVs in an indoor environment, and this is achieved by solving cost functions that consist of the LiDAR and IMU residuals. Compared to the traditional LIO approach, the initialization process of the developed RTLIO can be achieved, even when the device is stationary. To further reduce the accumulated pose errors, loop closure and pose-graph optimization are also developed in RTLIO. To demonstrate the efficacy of the developed RTLIO, experiments with long-range trajectory are conducted, and the results indicate that the RTLIO can outperform LIO with a smaller drift. Experiments with odometry benchmark dataset (i.e., KITTI) are also conducted to compare the performance with other methods, and the results show that the RTLIO can outperform ALOAM and LOAM in terms of exhibiting a smaller time delay and greater position accuracy. |
topic |
LiDAR-inertial odometry state estimation sensor fusion SLAM |
url |
https://www.mdpi.com/1424-8220/21/12/3955 |
work_keys_str_mv |
AT jungchengyang rtliorealtimelidarinertialodometryandmappingforuavs AT chunjunglin rtliorealtimelidarinertialodometryandmappingforuavs AT bingyuanyou rtliorealtimelidarinertialodometryandmappingforuavs AT yinlongyan rtliorealtimelidarinertialodometryandmappingforuavs AT tenghucheng rtliorealtimelidarinertialodometryandmappingforuavs |
_version_ |
1721350922484842496 |