Application of Kalman Filter to Improve 3D LiDAR Signals of Autonomous Vehicles in Adverse Weather

Abstract

A worldwide increase in the number of vehicles on the road has led to an increase in the frequency of serious traffic accidents, causing loss of life and property. Autonomous vehicles could be part of the solution, but their safe operation is dependent on the onboard LiDAR (light detection and ranging) systems used for the detection of the environment outside the vehicle. Unfortunately, problems with the application of LiDAR in autonomous vehicles remain, for example, the weakening of the echo detection capability in adverse weather conditions. The signal is also affected, even drowned out, by sensory noise outside the vehicles, and the problem can become so severe that the autonomous vehicle cannot move. Clearly, the accuracy of the stereo images sensed by the LiDAR must be improved. In this study, we developed a method to improve the acquisition of LiDAR data in adverse weather by using a combination of a Kalman filter and nearby point cloud denoising. The overall LiDAR framework was tested in experiments in a space 2 m in length and width and 0.6 m high. Normal weather and three kinds of adverse weather conditions (rain, thick smoke, and rain and thick smoke) were simulated. The results show that this system can be used to recover normal weather data from data measured by LiDAR even in adverse weather conditions. The results showed an effective improvement of 10% to 30% in the LiDAR stereo images. This method can be developed and widely applied in the future.

Mechanical energy harvesting in traffic environment and its application in smart transportation

“…On the one hand, the performance and stability of the sensor is compromised in poor weather conditions. On the other hand, it is difficult to achieve target detection beyond the line of sight in close-range vehicle following environment [179,180].…”

A Probabilistic Approach to Estimating Allowed SNR Values for Automotive LiDARs in “Smart Cities” under Various External Influences

“…Lin and Wu [4] proposed the use of the combination of the nearest neighbor segmentation algorithm and the Kalman filter for noise filtering purposes. Significant improvement in LiDAR detection (Velodyne LiDAR PUCK VLP-16) has been achieved (given as the root mean square error): 11-16% in rain and smoke, 15-23% in smoky weather, and 33-35% for rainy conditions.…”

“…In addition, a number of problems related to the use of LiDARs in various weather conditions, times of day and year remains unresolved. In particular, LiDAR's functioning can be influenced by the sunlight, low temperatures, and harsh weather conditions, e.g., rain, snow, fog, wind, etc., resulting in their performance degradation [3][4][5].…”

Point cloud denoising by reduction of ranging error in laser incidence direction

“…Current research in point cloud noise reduction predominantly employs statistical approaches [4], which may lead to the degradation or distortion of the original point cloud data. Common statistical noise reduction techniques include K-means [5], smoothing [6], octree [7], Kalman [8], Gaussian [9], [10], and bilateral filtering [11]. These methods generally work by either assessing the proximity of points to their neighbors to identify noise (as in K-means and smoothing filtering) or by aggregating points within a defined structure (like voxels in octree filtering) and then refining these points based on their statistical properties.…”