Factors to Evaluate Capability of Map for Vehicle Localization
Reads0
Chats0
TLDR
Four criteria for the self-localization ability of the map are introduced, based on normal distribution map which is a map format of normal distribution transformation scan-matching, and evaluated in Shinjuku, Japan.Abstract:
Recently, autonomous vehicle’s self-localization based on the matching of laser scanner data to the high definition (HD) map become more popular due to the availability of HD map and price down of light detection and ranging technologies. Many types of research have been done to achieve locally and globally accurate HD map for accurate localization. However, the global accuracy of the map does not guarantee accurate self-localization within the map. To achieve accurate self-localization, the map should satisfy some requirements. In this paper, the focus is made on the map, as one of the high potential sources of error in localization. By investigating the erroneous scenarios in the map and comparing their characteristics, we introduced four criteria for the self-localization ability of the map. These criteria are feature sufficiency, layout, local similarity, and representation quality of the map. Then, in order to quantify these criteria, we introduce several factors for each criterion. Unlike evaluation criteria which are defined regardless of the map formats, factors are defined based on normal distribution map which is a map format of normal distribution transformation scan-matching. These factors are calculated for each position in the map, based on the map features within its local vicinity. By conducting the experiments in Shinjuku, Japan, we have evaluated these factors in a different part of the map with different scenarios by comparing them with the self-localization error.read more
Citations
More filters
Journal ArticleDOI
Under-canopy UAV laser scanning for accurate forest field measurements
Eric Hyyppä,Juha Hyyppä,Juha Hyyppä,Teemu Hakala,Antero Kukko,Antero Kukko,Michael A. Wulder,Joanne C. White,Jiri Pyörälä,Xiaowei Yu,Yunsheng Wang,Juho-Pekka Virtanen,Juho-Pekka Virtanen,Onni Pohjavirta,Xinlian Liang,Markus Holopainen,Harri Kaartinen,Harri Kaartinen +17 more
TL;DR: In this article, a tree detection and stem curve estimation using laser scanning data obtained with an under-canopy flying UAV is presented, in which a Kaarta Stencil-1 laser scanner with an integrated simultaneous localization and mapping (SLAM) system is mounted on board an UAV that was manually piloted with the help of video goggles receiving a live video feed from the onboard camera of the UAV.
Journal ArticleDOI
High Definition 3D Map Creation Using GNSS/IMU/LiDAR Sensor Integration to Support Autonomous Vehicle Navigation.
Veli Ilçi,Charles K. Toth +1 more
TL;DR: The main objective was to assess the performance potential of autonomous vehicle sensor systems to obtain high-definition maps based on only using Velodyne sensor data for creating accurate point clouds, and results have confirmed that cm-level accuracy can be achieved.
Journal ArticleDOI
Mapping for Autonomous Driving: Opportunities and Challenges
TL;DR: This article provides a review of the production and uses of maps for autonomous driving and a synthesis of the opportunities and challenges and closes with 11 open research challenges for mapping for autonomousdriving.
Journal ArticleDOI
An Efficient V2X Based Vehicle Localization Using Single RSU and Single Receiver
TL;DR: A vehicle-to-infrastructure (V2I)-based vehicle localization algorithm is proposed that is low-cost and hardware requirements are simplified, the minimum requirement is a single roadside unit and single on-board receiver, and the available V2I information is formulated as an over-determined system.
Proceedings ArticleDOI
Robust Localization with Low-Mounted Multiple LiDARs in Urban Environments
Mahmut Demir,Kikuo Fujimura +1 more
TL;DR: In this paper, a robust, real-time and scalable localization framework for multi-LiDAR equipped vehicles in challenging urban environments is proposed and LiDAR localization is fused with the dead reckoning using the probabilistic scan matching confidence estimation method.
References
More filters
Proceedings ArticleDOI
Efficient variants of the ICP algorithm
Szymon Rusinkiewicz,Marc Levoy +1 more
TL;DR: An implementation is demonstrated that is able to align two range images in a few tens of milliseconds, assuming a good initial guess, and has potential application to real-time 3D model acquisition and model-based tracking.
Proceedings ArticleDOI
LOAM: Lidar Odometry and Mapping in Real-time
Ji Zhang,Sanjiv Singh +1 more
TL;DR: The method achieves both low-drift and low-computational complexity without the need for high accuracy ranging or inertial measurements and can achieve accuracy at the level of state of the art offline batch methods.
Proceedings ArticleDOI
The normal distributions transform: a new approach to laser scan matching
Peter Biber,Wolfgang Strasser +1 more
TL;DR: First results on real data demonstrate, that the normal distributions transform algorithm is capable to map unmodified indoor environments reliable and in real time, even without using odometry data.
Journal ArticleDOI
Scan registration for autonomous mining vehicles using 3D-NDT
TL;DR: Scan registration is an essential sub-task when building maps based on range finder data from mobile robots, and the problem is to deduce how the robot has moved between consecutive scans, based on the data collected.
Proceedings ArticleDOI
An ICP variant using a point-to-line metric
TL;DR: PLICP as discussed by the authors is an iterative closest/corresponding point (ICP) variant that uses a point-to-line metric, and an exact closed-form for minimizing such metric.
Related Papers (5)
The normal distributions transform: a new approach to laser scan matching
Peter Biber,Wolfgang Strasser +1 more