Robust Intensity-Based Localization Method for Autonomous Driving on Snow–Wet Road Surface

Abstract

Autonomous vehicles are being developed rapidly in recent years. In advance implementation stages, many particular problems must be solved to bring this technology into the market place. This paper focuses on the problem of driving in snow and wet road surface environments. First, the quality of LIDAR reflectivity decreases on wet road surfaces. Therefore, an accumulation strategy is designed to increase the density of online LIDAR images. In order enhance the texture of the accumulated images, Principal Component Analysis (PCA) is used to understand the geometrical structures and texture patterns in the map images. The LIDAR images are then reconstructed using the leading principal components with respect to the variance distribution accounted by each eigenvector. Second, the appearance of snow lines deforms the expected road context in LIDAR images. Accordingly, the edge profiles of the LIDAR and map images are extracted to encode the lane lines and roadside edges. Edge matching between the two profiles is then calculated to improve localization in the lateral direction. The proposed method has been tested and evaluated using real data that collected during the winter of 2016/2017 in Suzu and Kanazawa, Japan. The experimental results show that the proposed method increases the robustness of autonomous driving on wet road surfaces, provides a stable performance in laterally localizing the vehicle in the presence of snow lines and significantly reduces the overall localization error at a speed of 60km/h. Index Terms-Autonomous vehicles, laser imaging detection and ranging (LIDAR), autonomous localization, principal component analysis (PCA), template matching.

LCPF: A Particle Filter Lidar SLAM System With Loop Detection and Correction

“…In (5), x [m] 0:t is the pose of m-th particle from time 0 to t. Sequence like this makes up a trajectory, and a map is obtained by superimposing lidar observation on this trajectory. To estimate the trajectory, we need a particle swarm that containing many possible trajectories: [1] 0:t , x [2] 0:t , . .…”

“…Most indoor localization is realized by Simultaneous Localization And Mapping (SLAM). In an unknown environment, the robot localize itself and build a map according to the information (2D or 3D lidar ranging readings [1] [2], infrared ranging readings [3], video data streams [4] [5], etc.) about the environment.…”

Trajectory optimization and state selection for urban automated driving

“…The above-mentioned planners require sensing information about ego-vehicle and surrounding objects. GNSS/INS and the self-localization module provide precise position, orientation, velocity and acceleration for the ego-vehicle [10][11][12]. Surrounding objects are recognized using the onboard sensors such as LiDAR, radar and camera.…”

Angle-Awareness Based Joint Cooperative Positioning and Warning for Intelligent Transportation Systems

“…), the laser imaging detection and ranging (LIDAR) technique can achieve a simultaneous localization and mapping for intelligent vehicles, which is now accepted by more and more industries [ 10 , 11 , 12 , 13 ]. However, there still exist some problems that cannot be neglected if it is to be commercialized [ 14 ]. The first one is the cost; and the second one, also the most important one, is that the quality of LIDAR images will deteriorate because of the weak reflectivity of the wet road surface, which results in some detected region disappearing in the LIDAR images (such a difference between LIDAR images and map images will affect the further similarity calculation); in addition, the irregular snow lines inside the lane and near the roadsides also can confuse the lane identifiability.…”