Efficient and adaptive lidar–visual–inertial odometry for agricultural unmanned ground vehicle. (29th April 2022)

• Record Type: Journal Article
• Authors: Zhao, Zixu; Zhang, Yucheng; Long, Long; Lu, Zaiwang; Shi, Jinglin
• Is Part Of: International journal of advanced robotic systems. Volume 19:Number 2(2022)
• Publication Date: 2022-04-29
• Subjects: Sensor-fusion -- SLAM -- localization -- agricultural UGV; Robotics -- Periodicals; Robotics; Periodicals; 629.892
• DOI: 10.1177/17298806221094925
• Languages: English
• ISSNs: 1729-8806

Abstract

The accuracy of agricultural unmanned ground vehicles' localization directly affects the accuracy of their navigation. However, due to the changeable environment and fewer features in the agricultural scene, it is challenging for these unmanned ground vehicles to localize precisely in global positioning system-denied areas with a single sensor. In this article, we present an efficient and adaptive sensor-fusion odometry framework based on simultaneous localization and mapping to handle the localization problems of agricultural unmanned ground vehicles without the assistance of a global positioning system. The framework leverages three kinds of sub-odometry (lidar odometry, visual odometry and inertial odometry) and automatically combines them depending on the environment to provide accurate pose estimation in real time. The combination of sub-odometry is implemented by trading off the robustness and the accuracy of pose estimation. The efficiency and adaptability are mainly reflected in the novel surfel-based iterative closest point method for lidar odometry we propose, which utilizes the changeable surfel radius range and the adaptive iterative closest point initialization to improve the accuracy of pose estimation in different environments. We test our system in various agricultural unmanned ground vehicles' working zones and some other open data sets, and the results prove that the proposed method shows better performance mainly in accuracy, efficiency and robustness, compared with the state-of-art methods.