Aiming at the problems of laser Simultaneous Localization and Mapping (SLAM), such as drift, low localization accuracy and map ghosting, which are prone to occur during localization and map construction in different structured environments, a tightly-coupled SLAM method of lidar and Inertial Measurement Unit (IMU) is proposed. Specifically, the initial position information provided by the IMU for the lidar is used to construct the joint error function of the IMU and the lidar, so as to achieve iterative position optimization. Following, the variety of the environmental structural information is determined, using a degradation detection method, based on the perturbation model, thus the selection of different methods for position estimation in different featured environments is achieved. Finally, when adequate number of structured features is obvious, the algorithm based online and surface feature matching, as proposed in LOAM, is used for motion estimation between neighboring frames. In the case of structural information lacking, the Iterative Closest Point (ICP) algorithm, containing distribution shape constraints, is introduced, to increase the constraints on the effective observation data, improving the stability and accuracy of the system, under the premise of ensuring that the system can run in real time. The results of extensive testing and validation in different scenarios, using open-source datasets, show that the proposed algorithm has higher accuracy and environmental adaptability, compared to other commonly used algorithms. In addition, real-vehicle tests, based on an autonomous self-driving car test platform, further demonstrate that the proposed method can be adapted to long-time drive and large-scale working environments, while it can operate steadily in real time.
Zhu et al. (Wed,) studied this question.