Department of Vehicle and Electrical Engineering, Army Engineering University of PLA, Shijiazhuang 050003, China.
Sensors (Basel). 2023 Apr 4;23(7):3744. doi: 10.3390/s23073744.
Quickly grasping the surrounding environment's information and the location of the vehicle is the key to achieving automatic driving. However, accurate and robust localization and mapping are still challenging for field vehicles and robots due to the characteristics of emptiness, terrain changeability, and Global Navigation Satellite System (GNSS)-denied in complex field environments. In this study, an LVI-SAM-based lidar, inertial, and visual fusion using simultaneous localization and mapping (SLAM) algorithm was proposed to solve the problem of localization and mapping for vehicles in such open, bumpy, and Global Positioning System (GPS)-denied field environments. In this method, a joint lidar front end of pose estimation and correction was designed using the Super4PCS, Iterative Closest Point (ICP), and Normal Distributions Transform (NDT) algorithms and their variants. The algorithm can balance localization accuracy and real-time performance by carrying out lower-frequency pose correction based on higher-frequency pose estimation. Experimental results from the complex field environment show that, compared with LVI-SAM, the proposed method can reduce the translational error of localization by about 4.7% and create a three-dimensional point cloud map of the environment in real time, realizing the high-precision and high-robustness localization and mapping of the vehicle in complex field environments.
快速获取周围环境信息和车辆位置是实现自动驾驶的关键。然而，由于空旷、地形多变以及全球导航卫星系统（GNSS）受限制等特点，对于野外车辆和机器人来说，准确和稳健的定位和建图仍然具有挑战性。在这项研究中，提出了一种基于 LVI-SAM 的激光雷达、惯性和视觉融合的同时定位与建图（SLAM）算法，以解决车辆在这种开放、崎岖且 GPS 受限制的野外环境中的定位和建图问题。在该方法中，设计了一种联合激光雷达前端姿态估计和修正，使用 Super4PCS、迭代最近点（ICP）和正态分布变换（NDT）算法及其变体。该算法通过基于更高频率姿态估计进行较低频率的姿态修正，可以平衡定位精度和实时性能。来自复杂野外环境的实验结果表明，与 LVI-SAM 相比，该方法可以将定位的平移误差减少约 4.7%，并实时创建环境的三维点云地图，实现车辆在复杂野外环境中的高精度和高稳健性定位和建图。
