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도심 자율주행을 위한 라이다 정지 장애물 지도 기반 위치 보정 알고리즘
노한석,이현성,이경수 사단법인 한국자동차안전학회 2022 자동차안전학회지 Vol.14 No.2
This paper presents LiDAR static obstacle map based vehicle position correction algorithm for urban autonomous driving. Real Time Kinematic (RTK) GPS is commonly used in highway automated vehicle systems. For urban automated vehicle systems, RTK GPS have some trouble in shaded area. Therefore, this paper represents a method to estimate the position of the host vehicle using AVM camera, front camera, LiDAR and low-cost GPS based on Extended Kalman Filter (EKF). Static obstacle map (STOM) is constructed only with static object based on Bayesian rule. To run the algorithm, HD map and Static obstacle reference map (STORM) must be prepared in advance. STORM is constructed by accumulating and voxelizing the static obstacle map (STOM). The algorithm consists of three main process. The first process is to acquire sensor data from low-cost GPS, AVM camera, front camera, and LiDAR. Second, low-cost GPS data is used to define initial point. Third, AVM camera, front camera, LiDAR point cloud matching to HD map and STORM is conducted using Normal Distribution Transformation (NDT) method. Third, position of the host vehicle position is corrected based on the Extended Kalman Filter (EKF).The proposed algorithm is implemented in the Linux Robot Operating System (ROS) environment and showed better performance than only lane-detection algorithm. It is expected to be more robust and accurate than raw lidar point cloud matching algorithm in autonomous driving.
조감도에서의 객체 움직임 및 환경 변화를 반영한 시공간 특징 결합 기반의 라이다 비디오 3차원 객체 검출
이준형(Junhyung Lee),고준호(Junho Koh),최준원(Jun Won Choi) 한국자동차공학회 2022 한국자동차공학회 부문종합 학술대회 Vol.2022 No.6
This paper proposes an online 3D video object detection model based on deep learning using a sequential LiDAR point set. By utilizing sequential data as input, our proposed method aims to generate Spatio-temporal information and overcome the inherent limitations of point cloud data, such as sparsity and irregular acquisition due to distance and occlusion. The proposed method, called ST-FF, performs Spatio-temporal feature fusion between a birds eye view (BEV) feature maps obtained from each LiDAR point set. ST-FF first captures the movement of objects and changes in the surrounding environment over time. Based on the captured motion information, information suitable for the current feature map is selectively extracted from the past feature map. Then the final feature map is obtained by aggregating the feature map at the target frame and the extracted feature maps from the past frame. Finally, the detection head generates 3D bounding boxes for the target frame using the final feature map. Experiments were conducted on the nuScenes dataset to validate the contributions of the proposed method. Higher performance was obtained in LiDAR-based video object detection with ST-FF than 3D object detectors based on a single point set. In addition, by applying the proposed method to the 3D object detectors based on a single point set, we demonstrate that our methods are applicable to the existing LiDAR-based detectors.
LiDAR를 활용한 ROS 기반 ACC 인지 시스템 및 NDT-Localization 시스템에 대한 연구
김상준(Sangjun Kim),길현준(Hyeonjun Gil),최윤중(Yunjung Choi),김정하(Jungha Kim) 한국자동차공학회 2022 한국자동차공학회 학술대회 및 전시회 Vol.2022 No.11
This paper proposes ROS-Based ACC Recognition System and NDT-Localization System using LiDAR Sensor. The platform selected as the sub-controller is ERP-42, which informs the current speed, steering angle, and driving mode of the vehicle. An industrial PC is combined on the vehicle and used as a upper-Level controller. In the upper- Level controller, object detection and localization through LiDAR Sensor are published in the topic form on ROS. The vehicle performs object detection and localization through real-time communication using the corresponding topic. In conclusion, this system enables object detection and localization for ACC with one PC. By developing this study, it will be possible to build a optimized perception system using LiDAR Sensor.
딥러닝을 활용한 NDT 기반 3D 포인트 클라우드 지도 생성 알고리즘
김수연(Suyeon Kim),권순웅(Soonwoong Kwon),김명준(Myungjoon Kim),김정하(Jungha Kim) 한국자동차공학회 2020 한국자동차공학회 부문종합 학술대회 Vol.2020 No.7
High-accuracy localization is very important for safe driving of autonomous vehicles. To implement localization of autonomous vehicles GPS is usually used, but there are disadvantages that it is difficult to use in a shaded area. To overcome this problem, a lot of research has been conducted on the method using LiDAR. A representative localization method using LiDAR is a map matching algorithm. A high-accuracy point cloud map is required for map matching, and the point cloud map contains unnecessary data such as dynamic obstacles. It is necessary to remove the dynamic object of the point cloud map because it can degrade the matching performance. To date, it was manually removed by a person. In this paper, we propose an algorithm for a method of generating a point cloud map with dynamic obstacle information removed using NDT and deep learning.