High Accuracy and Low Complexity Lidar Place Recognition Using Unitary Invariant Frobenius Norm

Simultaneous localization and mapping (SLAM) is used in solving the problems of localization, navigation, and map construction for autonomous vehicles moving in unknown environments. Place recognition is an inevitable subject in SLAM, and the current lidar-based methods have been popularized for their rising environmental robustness. Currently, the place recognition method with lidar has attracted much attention due to its high environmental robustness. However, many 3-D laser point cloud methods for place recognition descriptors construction with the local or global point-cloud data omit some intrinsic properties of the point clouds. In this article, we propose a place recognition method based on the unitary invariance of the Frobenius norm for utilizing different attributes of ground points and non-ground points. Specifically, the interpretable filtering framework and a dynamic threshold adjustment strategy are raised according to different environments and sensors with intensity and geometric information. As commissioning, the comparative experiments are conducted with CHDloop datasets and public KITTI datasets of different scales and access types. Compared to the original scan context (SC) and intensity SC (ISC) methods, our proposed method achieves higher efficiency while maintaining an improved recall rate and precision. This novel method has been integrated into the existing lidar SLAM and formed a new framework FSC_ALOAM that reduces drifts in point-cloud mapping. The entire process improves the accordance of maps at the identical position in auto-driving.

I. Introduction

Simultaneous localization and mapping (SLAM) [1] plays a significant role in the field of autonomous driving [2]. Since the global navigation satellite system can easily be affected by environmental occlusion and multipath effects while the map-based localization stays robust [3], most autonomous vehicles depend on the localization information provided by SLAM [4]. Although drifts are inevitable in the entire SLAM processing and usually militate against the estimated state accompanied by trajectory [5], the impact of drifts can be effectively eliminated by visiting the same place multiple times [6] and thereby creating a consistent map. Place recognition is also known as loop detection in SLAM [7], which is critical for identifying and returning to the same position. In the past 20 years, many studies have established vision-based methods and shown the practicability in actual cases. However, the performances of these methods are relatively unstable because of the changes in recurrent viewpoint and transformations in light intensity. In contrast, the active sensor lidar is less susceptible to being generalized for place-recognizing tasks recently.