Citation: Yang, L.; Ma, H.; Wang, Y.;
Xia, J.; Wang, C. A Tightly Coupled
LiDAR-Inertial SLAM for Perceptually
Degraded Scenes. Sensors 2022, 22,
3063. https://doi.org/10.3390/
s22083063
Academic Editors: Luis Payá, Oscar
Reinoso García and Helder
Jesus Arajo
Received: 21 March 2022
Accepted: 14 April 2022
Published: 15 April 2022
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Article
A Tightly Coupled LiDAR-Inertial SLAM for Perceptually
Degraded Scenes
Lin Yang
1,2,
* , Hongwei Ma
1,2
, Yan Wang
1,2
, Jing Xia
1,2
and Chuanwei Wang
1,2
1
School of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, China;
mahw@xust.edu.cn (H.M.); 16105301002@stu.xust.edu.cn (Y.W.); xiajing1984@xust.edu.cn (J.X.);
wangchuanwei228@xust.edu.cn (C.W.)
2
Shaanxi Key Laboratory of Mine Electromechanical Equipment Intelligent Monitoring, Xi’an 710054, China
* Correspondence: 17101016007@stu.xust.edu.cn
Abstract:
Realizing robust six degrees of freedom (6DOF) state estimation and high-performance
simultaneous localization and mapping (SLAM) for perceptually degraded scenes (such as under-
ground tunnels, corridors, and roadways) is a challenge in robotics. To solve these problems, we
propose a SLAM algorithm based on tightly coupled LiDAR-IMU fusion, which consists of two
parts: front end iterative Kalman filtering and back end pose graph optimization. Firstly, on the
front end, an iterative Kalman filter is established to construct a tightly coupled LiDAR-Inertial
Odometry (LIO). The state propagation process for the a priori position and attitude of a robot,
which uses predictions and observations, increases the accuracy of the attitude and enhances the
system robustness. Second, on the back end, we deploy a keyframe selection strategy to meet the
real-time requirements of large-scale scenes. Moreover, loop detection and ground constraints are
added to the tightly coupled framework, thereby further improving the overall accuracy of the 6DOF
state estimation. Finally, the performance of the algorithm is verified using a public dataset and the
dataset we collected. The experimental results show that for perceptually degraded scenes, compared
with existing LiDAR-SLAM algorithms, our proposed algorithm grants the robot higher accuracy,
real-time performance and robustness, effectively reducing the cumulative error of the system and
ensuring the global consistency of the constructed maps.
Keywords: perceptually degraded scenes; LiDAR; IMU; state estimation; SLAM
1. Introduction
SLAM is the basis for autonomous navigation in robots [
1
], which use various sensors
to conduct real-time 6DOF state estimation in 3D space; this state estimation is the key
to achieving high-performance SLAM. The LiDAR-based approach can obtain accurate
range information for a scene in 3D space in real time and is invariant to ambient lighting
conditions. However, due to the problems of motion distortion [
2
], low-frequency updates,
and sparse point clouds [
3
] in LiDAR measurements, pure LiDAR is unsuitable for robots
dealing with aggressive movements or repetitive structures, such as tunnels or narrow
corridors. The shortcomings of LiDAR can be compensated for by fusing IMU. Unlike
LiDAR, IMU is not affected by drastic changes in structural features or the environment and
can provide high-precision pose estimation with high frequency in a short time. However,
due to noise and the bias of IMU sensors, error accumulation can drift over time. To
overcome these drawbacks of independent sensors, the reliability of state estimation [
4
,
5
] can
be improved by fusing multiple sensors together. Thus, the robustness of state estimation
can be effectively improved through tightly coupled LIO. Many studies have shown that
introducing loop detection in SLAM systems can effectively resolve pose drift caused
by the accumulation of sensor errors [
6
,
7
]. After detecting the loop frame, the pose is
continuously optimized to ensure the global consistency of the map, which greatly improves
the positioning accuracy.
Sensors 2022, 22, 3063. https://doi.org/10.3390/s22083063 https://www.mdpi.com/journal/sensors
