Traditional LiDAR odometry (LO) systems mainly leverage geometric information obtained from the traversed surroundings to register laser scans and estimate LiDAR ego-motion, while it may be unreliable in dynamic or unstructured environments. This paper proposes InTEn-LOAM, a low-drift and robust LiDAR odometry and mapping method that fully exploits implicit information of laser sweeps (i.e., geometric, intensity, and temporal characteristics). Scanned points are projected to cylindrical images, which facilitate the efficient and adaptive extraction of various types of features, i.e., ground, beam, facade, and reflector. We propose a novel intensity-based points registration algorithm and incorporate it into the LiDAR odometry, enabling the LO system to jointly estimate the LiDAR ego-motion using both geometric and intensity feature points. To eliminate the interference of dynamic objects, we propose a temporal-based dynamic object removal approach to filter them out before map update. Moreover, the local map is organized and downsampled using a temporal-related voxel grid filter to maintain the similarity between the current scan and the static local map. Extensive experiments are conducted on both simulated and real-world datasets. The results show that the proposed method achieves similar or better accuracy w.r.t the state-of-the-arts in normal driving scenarios and outperforms geometric-based LO in unstructured environments.
翻译:传统LIDAR odology (LO) 系统主要利用从环绕环境获得的几何信息来登记激光扫描和估计LIDAR 自我感动,而在动态或无结构的环境中,这些信息可能不可靠。本文提出InTEn-LOAM,这是一个低轨和强力的LIDAR 光学和绘图方法,充分利用激光扫描(即几何、强度和时间特性)的隐含信息。扫描点被预测为圆柱形图像,这有利于有效和适应性地提取各种特征,例如地面、横梁、外观和反射器。我们提出一个新的基于强度点的密度点登记算法,并将其纳入LIDAR odography,使LIDAR 自我感动法系统能够利用几何和强度特征点共同估计激光扫描(即几何、强度和时间特性)的隐含信息。我们提出一个基于时间的动态物体清除方法,以便在更新地图基础之前将其过滤出来。此外,当地地图使用非与时间有关的 voxel 矩格过滤器对各种特征进行组织和降为下版图,以维持当前和模拟的模拟结果。在目前和模拟的地图上进行更精确的扫描和模拟的模拟的模拟试验,在目前和模拟试验中进行较近的模拟的模拟数据中,比较的模拟的扫描和模拟中,以取得比较式的模拟结果。