RTLIO: Real-Time LiDAR-Inertial Odometry and Mapping for UAVs.

Jung-Cheng Yang,Chun-Jung Lin,Yin-Long Yan,Bing-Yuan You,Teng-Hu Cheng

doi:10.3390/s21123955

Jung-Cheng Yang, Chun-Jung Lin + Show 3 more

Open Access

https://doi.org/10.3390/s21123955

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Abstract

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.

Highlights

The real-time LiDAR inertial odometer system (RTLIO) generated two types of odometry defined in Section 1.3: (i) LiDAR-rate pose, and (ii) the inertial measurement units (IMUs)-rate pose, which can be obtained with minimal delay
The RTLIO developed in this work can generate accurate and reliable odometry information in real time, and the initialization process is performed when the Unmanned aerial vehicles (UAVs) is already in motion
The developed RTLIO method uses LiDAR and IMU to generate high-frequency odometry with improved performance compared to the methods that only use LiDARs

Summary

Introduction

There are limitations associated with each type of sensor, such as minimum illumination requirements and the presence of noise. To overcome these shortcomings of stand-alone sensors, multiple sensors have been used to increase the reliability of estimation [5,6,7,8,9]. The methods utilizing multiple sensors for state estimation are categorized into two types: loosely coupled (cf [5,6]) and tightly coupled (cf [7,8,9]). The tightly coupled approach directly fuses LiDAR and inertial measurements through a joint optimization that minimizes some residuals, whereas the loosely coupled approach deals with the multiple sensors separately. The tightly coupled method is less computationally efficient and more difficult to implement than the loosely coupled approach, but it is more robust in its approach to noise and more accurate [8]

Methods

Results

Conclusion