A Geodetic Normal Distribution Map for Long-Term LiDAR Localization on Earth

Kim, Chansoo; Cho, Sungjin; Sunwoo, Myoungho; Resende, Paulo; Bradai, Benazouz; Jo, Kichun

doi:10.1109/access.2020.3047421

Cited by 5 publications

(4 citation statements)

References 43 publications

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“…In the context of Earth‐scale mapping, Kim et al (2021) present a Geodetic Normal Distribution (GND) map structure. A geodetic quad‐tree tiling organizes the Earth's surface into spatial tiles with the same angular size in latitude and longitude directions.…”

Section: Discussionmentioning

confidence: 99%

A systematic literature review on long‐term localization and mapping for mobile robots

Sousa

Sobreira

Moreira

2023

Journal of Field Robotics

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Long‐term operation of robots creates new challenges to Simultaneous Localization and Mapping (SLAM) algorithms. Long‐term SLAM algorithms should adapt to recent changes while preserving older states, when dealing with appearance variations (lighting, daytime, weather, or seasonal) or environment reconfiguration. When also operating robots for long periods and trajectory lengths, the map should readjust to environment changes but not grow indefinitely. The map size should depend only on updating the map with new information of interest, not on the operation time or trajectory length. Although several studies in the literature review SLAM algorithms, none of the studies focus on the challenges associated to lifelong SLAM. Thus, this paper presents a systematic literature review on long‐term localization and mapping following the Preferred Reporting Items for Systematic reviews and Meta‐Analysis guidelines. The review analyzes 142 works covering appearance invariance, modeling the environment dynamics, map size management, multisession, and computational topics such as parallel computing and timing efficiency. The analysis also focus on the experimental data and evaluation metrics commonly used to assess long‐term autonomy. Moreover, an overview over the bibliographic data of the 142 records provides analysis in terms of keywords and authorship co‐occurrence to identify the terms more used in long‐term SLAM and research networks between authors, respectively. Future studies can update this paper thanks to the systematic methodology presented in the review and the public GitHub repository with all the documentation and scripts used during the review process.

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Section: Discussionmentioning

confidence: 99%

A systematic literature review on long‐term localization and mapping for mobile robots

Sousa

Sobreira

Moreira

2023

Journal of Field Robotics

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“…The NDT algorithm [34]- [38] is used to relocate maps with deviations [39], [40]. Instead of comparing the difference between point clouds and points in two cities, NDT first converts a high-precision map of a city into a normal distribution of multidimensional variables.…”

Section: B Relocation Algorithmmentioning

confidence: 99%

Setting Method of Downsampling Factor and Grid Factor for NDT Relocation Algorithm in Dynamic Environment

Lan

Kong

et al. 2022

IEEE Access

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“…To compress the map-relevant LiDAR data between the crowd-sourcing vehicles and the map cloud server, a concept of a Normal Distribution Transform (NDT) map is used in the paper. Especially, a Geodetic Normal Distribution (GND) map structure [16], which is extended from the NDT map in our previous research, is adopted because it supports a unified map structure for multiple vehicles. Based on the worldwide management property of the GND map, the map data are interpreted in the same manner in individual vehicles regardless of the coordinate conversion errors.…”

Section: Introductionmentioning

confidence: 99%

Cloud Update of Geodetic Normal Distribution Map Based on Crowd-Sourcing Detection against Road Environment Changes

Kim

Cho²,

Sunwoo

et al. 2022

Journal of Advanced Transportation

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LiDAR-based localization has been widely used for the pose estimation of autonomous vehicles. Since the localization requires a sustainable map reflecting environment changes, a map update framework based on crowd-sourcing measurements has been researched. Unfortunately, a point cloud map occupies too large data size to transmit data in the uploading and downloading of the map update framework. To realize the LiDAR map update framework by reducing the data size, we proposed a novel map update framework using a Geodetic Normal Distribution (GND) map that compresses the point cloud to the normal distributions. The proposed GND map update framework comprises two parts: map change detection based on crowd-sourcing vehicles and map updating based on a map cloud server. GND map changes are detected based on an evidence theory considering geometric relationships between the GND map and crowd-sourcing measurements and uploaded to the map cloud server. Uploaded map changes reproduce representative map changes based on a similarity-based clustering, which are updated into the GND map. The proposed framework was evaluated in simulations and real environments on construction sites. As a result, although partial map changes occurred, the GND map was kept up-to-date through the proposed framework and the localization for autonomous driving was performed successfully.

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A Geodetic Normal Distribution Map for Long-Term LiDAR Localization on Earth

Cited by 5 publications

References 43 publications

A systematic literature review on long‐term localization and mapping for mobile robots

A systematic literature review on long‐term localization and mapping for mobile robots

Setting Method of Downsampling Factor and Grid Factor for NDT Relocation Algorithm in Dynamic Environment

Cloud Update of Geodetic Normal Distribution Map Based on Crowd-Sourcing Detection against Road Environment Changes

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