Simulation-based lidar super-resolution for ground vehicles

Shan, Tixiao; Wang, Jinkun; Chen, Fanfei; Szenher, Paul; Englot, Brendan

doi:10.1016/j.robot.2020.103647

Cited by 56 publications

(64 citation statements)

References 25 publications

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“…In addition, simulation capabilities for lidar have advanced in recent years, with Yun et al [ 15 ] using lidar simulation to optimize and virtualize scanning patterns for lidar in the detection of total leaf area in tree crowns. Furthermore, Shan et al [ 16 ] have recently proposed a simulation-based method for achieving “super-resolution” by combining multiple virtual lidar sensor feeds.…”

Section: Related Workmentioning

confidence: 99%

An Analytic Model for Negative Obstacle Detection with Lidar and Numerical Validation Using Physics-Based Simulation

Goodin

Carrillo

Monroe

et al. 2021

Sensors

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Negative obstacles have long been a challenging aspect of autonomous navigation for ground vehicles. However, as terrestrial lidar sensors have become lighter and less costly, they have increasingly been deployed on small, low-flying UAV, affording an opportunity to use these sensors to aid in autonomous navigation. In this work, we develop an analytical model for predicting the ability of UAV or UGV mounted lidar sensors to detect negative obstacles. This analytical model improves upon past work in this area because it takes the sensor rotation rate and vehicle speed into account, as well as being valid for both large and small view angles. This analytical model is used to predict the influence of velocity on detection range for a negative obstacle and determine a limiting speed when accounting for vehicle stopping distance. Finally, the analytical model is validated with a physics-based simulator in realistic terrain. The results indicate that the analytical model is valid for altitudes above 10 m and show that there are drastic improvements in negative obstacle detection when using a UAV-mounted lidar. It is shown that negative obstacle detection ranges for various UAV-mounted lidar are 60–110 m, depending on the speed of the UAV and the type of lidar used. In contrast, detection ranges for UGV mounted lidar are found to be less than 10 m.

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Section: Related Workmentioning

confidence: 99%

An Analytic Model for Negative Obstacle Detection with Lidar and Numerical Validation Using Physics-Based Simulation

Goodin

Carrillo

Monroe

et al. 2021

Sensors

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“…LIO-SAM algorithm [4] is a factor graph optimization algorithm based on LiDAR, IMU and GPS sensors proposed by Tixiao Shan, et. al.…”

Section: Point Cloud Map Construction Based On Lio-sam Algorithmmentioning

confidence: 99%

Research on NDT-based Positioning for Autonomous Driving

Liu

Luo

et al. 2021

E3S Web Conf.

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Autonomous driving technology is one of the currently popular technologies, while positioning is the basic problem of autonomous navigation of autonomous vehicles. GPS is widely used as a relatively mature solution in the outdoor open road environment. However, GPS signals will be greatly affected in a complex environment with obstruction and electromagnetic interference, even signal loss may occur if serious, which has a great impact on the accuracy, stability and reliability of positioning. For the time being, L4 and most L3 autonomous driving modules still provide registration and positioning based on the high-precision map constructed. Based on this, this paper elaborates on the reconstruction of the experimental scene environment, using the SLAM (simultaneous localization and mapping) method to construct a highprecision point cloud map. On the constructed prior map, the 3D laser point cloud NDT matching method is used for real-time positioning, which is tested and verified on the “JAC Electric Vehicle” platform. The experimental results show that this algorithm has high positioning accuracy and its real-time performance meets the requirements, which can replace GPS signals to complete the positioning of autonomous vehicles when there is no GPS signal or the GPS signal is weak, and provide positioning accuracy meeting the requirements.

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“…Besides, there are some important works that deal with the use of LiDAR point cloud in the general level and through the possibility of 3D LiDAR for autonomous vehicles [28,29]. M. B. Gergelova et al present "Identification of Roof Surfaces from LiDAR Cloud Points by GIS Tools", which focused on the topic of identifying the roof areas of residential buildings to operatively determine the essential characteristics of these buildings in the construction of smart cities [28].…”

Section: Introductionmentioning

confidence: 99%

“…The effective solution represents a suitable basis for possible application for solar systems or green roofs in the field of building smart cities [28]. T. Shan et al proposed Robotics and Autonomous Systems [29] based on a LiDAR super-resolution method to produce high resolution point clouds with high accuracy [29]. The approach achieves superior accuracy in the end-stage maps produced, as compared with both deep learning methods and interpolation methods [29].…”

Section: Introductionmentioning

confidence: 99%

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Three-Dimensional LiDAR Decoder Design for Autonomous Vehicles in Smart Cities

Fan

Wang

2022

Information

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With the advancement of artificial intelligence, deep learning technology is applied in many fields. The autonomous car system is one of the most important application areas of artificial intelligence. LiDAR (Light Detection and Ranging) is one of the most critical components of self-driving cars. LiDAR can quickly scan the environment to obtain a large amount of high-precision three-dimensional depth information. Self-driving cars use LiDAR to reconstruct the three-dimensional environment. The autonomous car system can identify various situations in the vicinity through the information provided by LiDAR and choose a safer route. This paper is based on Velodyne HDL-64 LiDAR to decode data packets of LiDAR. The decoder we designed converts the information of the original data packet into X, Y, and Z point cloud data so that the autonomous vehicle can use the decoded information to reconstruct the three-dimensional environment and perform object detection and object classification. In order to prove the performance of the proposed LiDAR decoder, we use the standard original packets used for the comparison of experimental data, which are all taken from the Map GMU (George Mason University). The average decoding time of a frame is 7.678 milliseconds. Compared to other methods, the proposed LiDAR decoder has higher decoding speed and efficiency.

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Simulation-based lidar super-resolution for ground vehicles

Cited by 56 publications

References 25 publications

An Analytic Model for Negative Obstacle Detection with Lidar and Numerical Validation Using Physics-Based Simulation

An Analytic Model for Negative Obstacle Detection with Lidar and Numerical Validation Using Physics-Based Simulation

Research on NDT-based Positioning for Autonomous Driving

Three-Dimensional LiDAR Decoder Design for Autonomous Vehicles in Smart Cities

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