LiDAR Data Noise Models and Methodology for Sim-to-Real Domain Generalization and Adaptation in Autonomous Driving Perception

2021 ◽  
Author(s):  
Joao Espadinha ◽  
Ivan Lebedev ◽  
Luka Lukic ◽  
Alexandre Bernardino
Keyword(s):  
Autonomous Driving ◽  
Lidar Data ◽  
Data Noise ◽  
Real Domain ◽  
Noise Models

Obstacle Detection for Autonomous Driving Vehicles With Multi-LiDAR Sensor Fusion

2019 ◽  
Vol 142 (2) ◽  
Author(s):  
Mingcong Cao ◽  
Junmin Wang
Keyword(s):  
Data Processing ◽  
Autonomous Vehicles ◽  
Hybrid Electric Vehicle ◽  
Spatial Clustering ◽  
Autonomous Driving ◽  
Obstacle Detection ◽  
Lidar Data ◽  
Detection Accuracy ◽  
Clustering And Classification ◽  
Systematic Solution

Abstract In contrast to the single-light detection and ranging (LiDAR) system, multi-LiDAR sensors may improve the environmental perception for autonomous vehicles. However, an elaborated guideline of multi-LiDAR data processing is absent in the existing literature. This paper presents a systematic solution for multi-LiDAR data processing, which orderly includes calibration, filtering, clustering, and classification. As the accuracy of obstacle detection is fundamentally determined by noise filtering and object clustering, this paper proposes a novel filtering algorithm and an improved clustering method within the multi-LiDAR framework. To be specific, the applied filtering approach is based on occupancy rates (ORs) of sampling points. Besides, ORs are derived from the sparse “feature seeds” in each searching space. For clustering, the density-based spatial clustering of applications with noise (DBSCAN) is improved with an adaptive searching (AS) algorithm for higher detection accuracy. Besides, more robust and accurate obstacle detection can be achieved by combining AS-DBSCAN with the proposed OR-based filtering. An indoor perception test and an on-road test were conducted on a fully instrumented autonomous hybrid electric vehicle. Experimental results have verified the effectiveness of the proposed algorithms, which facilitate a reliable and applicable solution for obstacle detection.

scholarly journals A Quantitative Analysis of Point Clouds from Automotive Lidars Exposed to Artificial Rain and Fog

Atmosphere ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 738
Author(s):  
Karl Montalban ◽  
Christophe Reymann ◽  
Dinesh Atchuthan ◽  
Paul-Edouard Dupouy ◽  
Nicolas Riviere ◽  
...  
Keyword(s):  
Quantitative Analysis ◽  
Point Clouds ◽  
Autonomous Driving ◽  
Light Detection And Ranging ◽  
Lidar Data ◽  
Climatic Chamber ◽  
Weather Events ◽  
Micro Motion ◽  
Artificial Rain ◽  
Rain Rates

Light Detection And Ranging sensors (lidar) are key to autonomous driving, but their data is severely impacted by weather events (rain, fog, snow). To increase the safety and availability of self-driving vehicles, the analysis of the phenomena of the consequences at stake is necessary. This paper presents experiments performed in a climatic chamber with lidars of different technologies (spinning, Risley prisms, micro-motion and MEMS) that are compared in various artificial rain and fog conditions. A specific target with calibrated reflectance is used to make a first quantitative analysis. We observe different results depending on the sensors, and unexpected behaviors in the analysis with artificial rain are seen where higher rain rates do not necessarily mean higher degradations on lidar data.

scholarly journals LiDAR Data Enrichment by Fusing Spatial and Temporal Adjacent Frames

2021 ◽  
Vol 13 (18) ◽  
pp. 3640
Author(s):  
Hao Fu ◽  
Hanzhang Xue ◽  
Xiaochang Hu ◽  
Bokai Liu
Keyword(s):  
Sparse Representation ◽  
Real Time ◽  
Point Cloud ◽  
Moving Objects ◽  
Autonomous Driving ◽  
Experimental Results ◽  
Lidar Data ◽  
Identification Algorithm ◽  
Data Enrichment ◽  
A New Technique

In autonomous driving scenarios, the point cloud generated by LiDAR is usually considered as an accurate but sparse representation. In order to enrich the LiDAR point cloud, this paper proposes a new technique that combines spatial adjacent frames and temporal adjacent frames. To eliminate the “ghost” artifacts caused by moving objects, a moving point identification algorithm is introduced that employs the comparison between range images. Experiments are performed on the publicly available Semantic KITTI dataset. Experimental results show that the proposed method outperforms most of the previous approaches. Compared with these previous works, the proposed method is the only method that can run in real-time for online usage.

Deep Learning Inspired Object Consolidation Approaches Using LiDAR Data for Autonomous Driving: A Review

2021 ◽  
Author(s):  
M. S. Mekala ◽  
Woongkyu Park ◽  
Gaurav Dhiman ◽  
Gautam Srivastava ◽  
Ju H. Park ◽  
...  
Keyword(s):  
Deep Learning ◽  
Autonomous Driving ◽  
Lidar Data

scholarly journals Real-Time Road Lane Detection in Urban Areas Using LiDAR Data

Electronics ◽  
2018 ◽  
Vol 7 (11) ◽  
pp. 276 ◽  
Author(s):  
Jiyoung Jung ◽  
Sung-Ho Bae
Keyword(s):  
Real Time ◽  
Urban Areas ◽  
Autonomous Driving ◽  
Lane Detection ◽  
Detection Methods ◽  
Lidar Data ◽  
The Road ◽  
Road Signs ◽  
Probe Vehicle ◽  
Map Generation

The generation of digital maps with lane-level resolution is rapidly becoming a necessity, as semi- or fully-autonomous driving vehicles are now commercially available. In this paper, we present a practical real-time working prototype for road lane detection using LiDAR data, which can be further extended to automatic lane-level map generation. Conventional lane detection methods are limited to simple road conditions and are not suitable for complex urban roads with various road signs on the ground. Given a 3D point cloud scanned by a 3D LiDAR sensor, we categorized the points of the drivable region and distinguished the points of the road signs on the ground. Then, we developed an expectation-maximization method to detect parallel lines and update the 3D line parameters in real time, as the probe vehicle equipped with the LiDAR sensor moved forward. The detected and recorded line parameters were integrated to build a lane-level digital map with the help of a GPS/INS sensor. The proposed system was tested to generate accurate lane-level maps of two complex urban routes. The experimental results showed that the proposed system was fast and practical in terms of effectively detecting road lines and generating lane-level maps.

Investigation of Lidar Data for Autonomous Driving with an Electric Bus

2019 ◽  
Vol 14 (3) ◽  
pp. 34-39 ◽  
Author(s):  
Christian Feller ◽  
Ulrich Haböck ◽  
Stefan Maier ◽  
Jochen Schwenninger
Keyword(s):  
Autonomous Driving ◽  
Lidar Data ◽  
Electric Bus

scholarly journals Pedestrian Detection Based on Panoramic Depth Map Transformed from 3D-LiDAR Data

2020 ◽  
Author(s):  
Guoqiang Chen ◽  
Zhuangzhuang Mao ◽  
Huailong Yi ◽  
Xiaofeng Li ◽  
Bingxin Bai ◽  
...  
Keyword(s):  
Pedestrian Detection ◽  
Depth Map ◽  
Point Clouds ◽  
Autonomous Driving ◽  
Detection Algorithm ◽  
Lidar Data ◽  
Average Precision ◽  
Depth Maps ◽  
3D Point Clouds ◽  
3D Lidar

Object detection is a crucial task of autonomous driving. This paper addresses an effective algorithm for pedestrian detection of the panoramic depth map transformed from the 3D-LiDAR data. Firstly, the 3D point clouds are transformed into panoramic depth maps, and then the panoramic depth maps are enhanced. Secondly, the grounds of the 3D point clouds are removed. The remaining point clouds are clustered, filtered and projected onto the previously generated panoramic depth maps, and new panoramic depth maps are obtained. Finally, the new panoramic depth maps are jointed to generate depth maps with different sizes, which are used as input of the improved PVANET for pedestrian detection. The 2D image of the panoramic depth map applied to the proposed algorithm is transformed from 3D point cloud, effectively containing the panorama of the sensor, and is more suitable for the environment perception of autonomous driving. Compared with the detection algorithm based on RGB images, the proposed algorithm cannot be affected by light, and can maintain the normal average precision of pedestrian detection at night. In order to increase the robustness of detecting small objects like pedestrians, the network structure based on the original PVANET is modified in this paper. A new dataset is built by processing the 3D-LiDAR data and the model trained on the new dataset perform well. The experimental results show that the proposed algorithm achieves high accuracy and robustness in pedestrian detection under different illumination conditions. Furthermore, when trained on the new dataset, the model exhibits average precision improvements of 2.8–5.1 % over the original PVANET, making it more suitable for autonomous driving applications.

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