Detailed Analysis on Generating the Range Image for LiDAR Point Cloud Processing

Wu, Tao; Fu, Hao; Liu, Bokai; Xue, Hanzhang; Ren, Ruike; Tu, Zhiming

doi:10.3390/electronics10111224

Cited by 14 publications

(8 citation statements)

References 26 publications

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“…Bogoslavskyi et al [ 109 ], running on a single CPU core, were able to perform the ground filtering in less than 4 ms using the KITTI dataset. Nonetheless, the method for creating range images is very dependent on the sensor and may affect the accuracy due to the needed sampling process [ 113 ].…”

Section: Ground Segmentation Methodsmentioning

confidence: 99%

A Survey on Ground Segmentation Methods for Automotive LiDAR Sensors

Gomes

Matias

Campos

et al. 2023

Sensors

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In the near future, autonomous vehicles with full self-driving features will populate our public roads. However, fully autonomous cars will require robust perception systems to safely navigate the environment, which includes cameras, RADAR devices, and Light Detection and Ranging (LiDAR) sensors. LiDAR is currently a key sensor for the future of autonomous driving since it can read the vehicle’s vicinity and provide a real-time 3D visualization of the surroundings through a point cloud representation. These features can assist the autonomous vehicle in several tasks, such as object identification and obstacle avoidance, accurate speed and distance measurements, road navigation, and more. However, it is crucial to detect the ground plane and road limits to safely navigate the environment, which requires extracting information from the point cloud to accurately detect common road boundaries. This article presents a survey of existing methods used to detect and extract ground points from LiDAR point clouds. It summarizes the already extensive literature and proposes a comprehensive taxonomy to help understand the current ground segmentation methods that can be used in automotive LiDAR sensors.

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Section: Ground Segmentation Methodsmentioning

confidence: 99%

A Survey on Ground Segmentation Methods for Automotive LiDAR Sensors

Gomes

Matias

Campos

et al. 2023

Sensors

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“…A Point cloud image is a group of point images in which signals reflected from objects are produced in the form of three-dimensional points, and have been widely applied to implement a digital twin of buildings or objects using laser scanners [16][17][18][19][20][21].…”

Section: Point Cloud Imagementioning

confidence: 99%

Implementation of MIMO Radar-Based Point Cloud Images for Environmental Recognition of Unmanned Vehicles and Its Application

Kim,

Khang,

Choi

et al. 2024

Remote Sensing

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High-performance radar systems are becoming increasingly popular for accurately detecting obstacles in front of unmanned vehicles in fog, snow, rain, night and other scenarios. The use of these systems is gradually expanding, such as indicating empty space and environment detection rather than just detecting and tracking the moving targets. In this paper, based on our high-resolution radar system, a three-dimensional point cloud image algorithm is developed and implemented. An axis translation and compensation algorithm is applied to minimize the point spreading caused by the different mounting positions and the alignment error of the Global Navigation Satellite System (GNSS) and radar. After applying the algorithm, a point cloud image for a corner reflector target and a parked vehicle is created to directly compare the improved results. A recently developed radar system is mounted on the vehicle and it collects data through actual road driving. Based on this, a three-dimensional point cloud image including an axis translation and compensation algorithm is created. As a results, not only the curbstones of the road but also street trees and walls are well represented. In addition, this point cloud image is made to overlap and align with an open source web browser (QtWeb)-based navigation map image to implement the imaging algorithm and thus determine the location of the vehicle. This application algorithm can be very useful for positioning unmanned vehicles in urban area where GNSS signals cannot be received due to a large number of buildings. Furthermore, sensor fusion, in which a three-dimensional point cloud radar image appears on the camera image, is also implemented. The position alignment of the sensors is realized through intrinsic and extrinsic parameter optimization. This high-performance radar application algorithm is expected to work well for unmanned ground or aerial vehicle route planning and avoidance maneuvers in emergencies regardless of weather conditions, as it can obtain detailed information on space and obstacles not only in the front but also around them.

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“…All of these papers describe the 3D object detectors trained on the raw LiDAR point clouds. Such a method requires a much more complicated topology of a deep neural network than the one proposed in this paper, where the range image [13,14] is the input for the algorithm. These differences are discussed in more detail in Section 4.3.…”

Section: Related Workmentioning

confidence: 99%

“…The idea presented in this paper is to simplify the problem to two dimensions, without losing precise information in all dimensions. This approach is known as the range image conversion [13,14].…”

Section: Synthetic Lidar Data Preparation For the Training Datasetmentioning

confidence: 99%

Comparison of Pedestrian Detectors for LiDAR Sensor Trained on Custom Synthetic, Real and Mixed Datasets

Jabłoński

Iwaniec

Zabierowski

2022

Sensors

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Deep learning algorithms for object detection used in autonomous vehicles require a huge amount of labeled data. Data collecting and labeling is time consuming and, most importantly, in most cases useful only for a single specific sensor application. Therefore, in the course of the research which is presented in this paper, the LiDAR pedestrian detection algorithm was trained on synthetically generated data and mixed (real and synthetic) datasets. The road environment was simulated with the application of the 3D rendering Carla engine, while the data for analysis were obtained from the LiDAR sensor model. In the proposed approach, the data generated by the simulator are automatically labeled, reshaped into range images and used as training data for a deep learning algorithm. Real data from Waymo open dataset are used to validate the performance of detectors trained on synthetic, real and mixed datasets. YOLOv4 neural network architecture is used for pedestrian detection from the LiDAR data. The goal of this paper is to verify if the synthetically generated data can improve the detector’s performance. Presented results prove that the YOLOv4 model trained on a custom mixed dataset achieved an increase in precision and recall of a few percent, giving an F1-score of 0.84.

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Detailed Analysis on Generating the Range Image for LiDAR Point Cloud Processing

Cited by 14 publications

References 26 publications

A Survey on Ground Segmentation Methods for Automotive LiDAR Sensors

A Survey on Ground Segmentation Methods for Automotive LiDAR Sensors

Implementation of MIMO Radar-Based Point Cloud Images for Environmental Recognition of Unmanned Vehicles and Its Application

Comparison of Pedestrian Detectors for LiDAR Sensor Trained on Custom Synthetic, Real and Mixed Datasets

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