Citation: Zhao, Z.; Zhang, Y.; Shi, J.;
Long, L.; Lu, Z. Robust Lidar-Inertial
Odometry with Ground Condition
Perception and Optimization
Algorithm for UGV. Sensors 2022, 22,
7424. https://doi.org/10.3390/
s22197424
Academic Editors: Luis Payá, Oscar
Reinoso García and Helder Jesus
Araújo
Received: 1 September 2022
Accepted: 26 September 2022
Published: 29 September 2022
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Article
Robust Lidar-Inertial Odometry with Ground Condition
Perception and Optimization Algorithm for UGV
Zixu Zhao
1,2,
* , Yucheng Zhang
1
, Jinglin Shi
1
, Long Long
1
and Zaiwang Lu
1,2
1
Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
2
University of Chinese Academy of Sciences, Beijing 100049, China
* Correspondence: zhaozixu18z@ict.ac.cn
Abstract:
Unmanned ground vehicles (UGVs) are making more and more progress in many ap-
plication scenarios in recent years, such as exploring unknown wild terrain, working in precision
agriculture and serving in emergency rescue. Due to the complex ground conditions and changeable
surroundings of these unstructured environments, it is challenging for these UGVs to obtain robust
and accurate state estimations by using sensor fusion odometry without prior perception and op-
timization for specific scenarios. In this paper, based on an error-state Kalman filter (ESKF) fusion
model, we propose a robust lidar-inertial odometry with a novel ground condition perception and
optimization algorithm specifically designed for UGVs. The probability distribution gained from
the raw inertial measurement unit (IMU) measurements during a certain time period and the state
estimation of ESKF were both utilized to evaluate the flatness of ground conditions in real-time;
then, by analyzing the relationship between the current ground condition and the accuracy of the
state estimation, the tightly coupled lidar-inertial odometry was dynamically optimized further by
adjusting the related parameters of the processing algorithm of the lidar points to obtain robust
and accurate ego-motion state estimations of UGVs. The method was validated in various types
of environments with changeable ground conditions, and the robustness and accuracy are shown
through the consistent accurate state estimation in different ground conditions compared with the
state-of-art lidar-inertial odometry systems.
Keywords: lidar-inertial odometry; ground perception; state estimation; sensor fusion; UGV
1. Introduction
With the rapid development of computer software technology and sensor hardware
technology, the state estimation of UGVs utilizing various types of sensors, such as lidar,
inertial measurement units (IMUs), stereo cameras and ultra-wide bands (UWBs), has been
studied by many researchers during the past decade, and multi-sensor suites and different
sensor fusion methods have been proposed that aim to improve the accuracy, efficiency and
robustness of UGVs’ ego-motion state estimation [
1
]. Due to the physical characteristics of
lidar-inertial odometry, it has a lower cost of computing resources and less degeneration in
tough environments [
2
] compared with the other types of odometry, such as visual-inertial
odometry. In addition, the fusion of lidar and IMU has been noticed and studied further for
usage in UGVs in recent years. Furthermore, new types of lidar with a low cost and smaller
size have also increasingly arisen in the market, such as solid-state lidar[
3
,
4
]. Based on the
above analysis, our work mainly revolves around the improvements and optimizations of
lidar-inertial odometry used in UGVs.
It is known that lidar odometry may degenerate in situations where large planar
areas and few geometry features exist, so inertial information is always used to main-
tain robustness and boost the frequency of the output for the odometry system. Inertial
information can provide valid pose transformation constraints and can be leveraged to
compensate for the spinning motion of lidar. Furthermore, it can also be utilized for the
Sensors 2022, 22, 7424. https://doi.org/10.3390/s22197424 https://www.mdpi.com/journal/sensors
