Low-Cost GPS-Aided LiDAR State Estimation and Map Building

Zheng, Linwei; Zhu, Yilong; Xue, Bohuan; Liu, Ming; Fan, Rui

doi:10.1109/ist48021.2019.9010530

Cited by 22 publications

(13 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Zheng et al [22] have presented a low-cost GPS-assisted LiDAR state estimation system for autonomous vehicle. In this literature, a LiDAR is employed to obtain highly precise 3D geometry data and an IMU is used to correct point cloud misalignment.…”

Section: Related Workmentioning

confidence: 99%

A Road Environment Prediction System for Intelligent Vehicle

Sun

Pei

et al. 2021

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

The road environment prediction is an essential task for intelligent vehicle. In this study, we provide a flexible system that focuses on freespace detection and road environment prediction to host vehicle. The hardware of this system includes two parts: a binocular camera and a low-power mobile platform, which is flexible and portable for a variety of intelligent vehicle. We put forward a multiscale stereo matching algorithm to reduce the computing cost of the hardware unit. Based on disparity space and points cloud, we propose a weighted probability grid map to detect freespace region and a state model to describe the road environment. The experiments show that the proposed system is accurate and robust, which indicates that this technique is fully competent for road environment prediction for intelligent vehicle.

show abstract

Section: Related Workmentioning

confidence: 99%

A Road Environment Prediction System for Intelligent Vehicle

Sun

Pei

et al. 2021

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

show abstract

“…In addition to the aforementioned passive and active sensors, GPS and IMU systems are commonly used to enhance autonomous car localization and mapping performance [6]. GPS can provide both time and geolocation information for autonomous cars.…”

Section: Car Sensorsmentioning

confidence: 99%

Computer Stereo Vision for Autonomous Driving

Fan,

Wang,

Bocus

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

As an important component of autonomous systems, autonomous car perception has had a big leap with recent advances in parallel computing architectures. With the use of tiny but full-feature embedded supercomputers, computer stereo vision has been prevalently applied in autonomous cars for depth perception. The two key aspects of computer stereo vision are speed and accuracy. They are both desirable but conflicting properties, as the algorithms with better disparity accuracy usually have higher computational complexity. Therefore, the main aim of developing a computer stereo vision algorithm for resource-limited hardware is to improve the trade-off between speed and accuracy. In this chapter, we introduce both the hardware and software aspects of computer stereo vision for autonomous car systems. Then, we discuss four autonomous car perception tasks, including 1) visual feature detection, description and matching, 2) 3D information acquisition, 3) object detection/recognition and 4) semantic image segmentation. The principles of computer stereo vision and parallel computing on multi-threading CPU and GPU architectures are then detailed.

show abstract

“…It is closely related to many critical self-driving subsystems, such as perception [7], decision [8,9], planning [10], control [1,11,12], and so on. Vehicle-mounted sensor positioning may have problems in scenes with sparse environmental features and dynamic changes in surrounding objects [13,14], such as campuses and ports.…”

Section: Introductionmentioning

confidence: 99%

ECPC-ICP: A 6D Vehicle Pose Estimation Method by Fusing the Roadside Lidar Point Cloud and Road Feature

Liu

Xiong

et al. 2021

Sensors

View full text Add to dashboard Cite

In the vehicle pose estimation task based on roadside Lidar in cooperative perception, the measurement distance, angle, and laser resolution directly affect the quality of the target point cloud. For incomplete and sparse point clouds, current methods are either less accurate in correspondences solved by local descriptors or not robust enough due to the reduction of effective boundary points. In response to the above weakness, this paper proposed a registration algorithm Environment Constraint Principal Component-Iterative Closest Point (ECPC-ICP), which integrated road information constraints. The road normal feature was extracted, and the principal component of the vehicle point cloud matrix under the road normal constraint was calculated as the initial pose result. Then, an accurate 6D pose was obtained through point-to-point ICP registration. According to the measurement characteristics of the roadside Lidars, this paper defined the point cloud sparseness description. The existing algorithms were tested on point cloud data with different sparseness. The simulated experimental results showed that the positioning MAE of ECPC-ICP was about 0.5% of the vehicle scale, the orientation MAE was about 0.26°, and the average registration success rate was 95.5%, which demonstrated an improvement in accuracy and robustness compared with current methods. In the real test environment, the positioning MAE was about 2.6% of the vehicle scale, and the average time cost was 53.19 ms, proving the accuracy and effectiveness of ECPC-ICP in practical applications.

show abstract

Low-Cost GPS-Aided LiDAR State Estimation and Map Building

Cited by 22 publications

References 28 publications

A Road Environment Prediction System for Intelligent Vehicle

A Road Environment Prediction System for Intelligent Vehicle

Computer Stereo Vision for Autonomous Driving

ECPC-ICP: A 6D Vehicle Pose Estimation Method by Fusing the Roadside Lidar Point Cloud and Road Feature

Contact Info

Product

Resources

About