Monocular localization within sparse voxel maps

Wong, David; Kawanishi, Yasutomo; Deguchi, Daisuke; Ide, Ichiro; Murase, Hiroshi

doi:10.1109/ivs.2017.7995767

Cited by 5 publications

(1 citation statement)

References 17 publications

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“…LiDAR localization within LiDAR maps (Ding et al, 2020;Egger et al, 2018;Fu et al, 2018;Levinson & Thrun, 2010;Wolcott & Eustice, 2017), visual localization within visual maps (Murartal & Tardos, 2017;Wong et al, 2017), visual localization within LiDAR maps (Wolcott & Eustice, 2014;Zuo et al, 2020), radar localization within radar maps (Saftescu et al, 2020) and radar localization within LiDAR maps (Yin et al, 2020). Among these approaches, LiDAR localization within LiDAR maps is usually considered to be the most accurate one (Kuutti et al, 2018), and is the focus of this paper.…”

Section: Introductionmentioning

confidence: 99%

LiDAR‐based robust localization for field autonomous vehicles in off‐road environments

Ren

Xue

et al. 2021

Journal of Field Robotics

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Robust localization is an essential capability for autonomous land vehicles. While a lot of work focused on structured environments, this article focuses on navigation in off-road environments. In the off-road environment, due to the lack of salient features, scan matching algorithms tend to degenerate. Therefore, the first contribution of this paper is to propose a reliable degeneracy indicator which can evaluate the scan matching performance. The evaluated degeneracy indicator is then integrated into the factor graph optimization framework which is used in both the offline mapping system and the online localization system. Moreover, a complete navigation system that can handle the incomplete and partly outdated LiDAR maps is developed. Extensive tests on real-world data sets show that the proposed system outperforms state-of-the-art approaches, especially in degenerate scenarios.

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Section: Introductionmentioning

confidence: 99%

LiDAR‐based robust localization for field autonomous vehicles in off‐road environments

Ren

Xue

et al. 2021

Journal of Field Robotics

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Multimodal localization: Stereo over LiDAR map

Zuo

Yang

et al. 2020

Journal of Field Robotics

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In this paper, we present a real‐time high‐precision visual localization system for an autonomous vehicle which employs only low‐cost stereo cameras to localize the vehicle with a priori map built using a more expensive 3D LiDAR sensor. To this end, we construct two different visual maps: a sparse feature visual map for visual odometry (VO) based motion tracking, and a semidense visual map for registration with the prior LiDAR map. To register two point clouds sourced from different modalities (i.e., cameras and LiDAR), we leverage probabilistic weighted normal distributions transformation (ProW‐NDT), by particularly taking into account the uncertainty of source point clouds. The registration results are then fused via pose graph optimization to correct the VO drift. Moreover, surfels extracted from the prior LiDAR map are used to refine the sparse 3D visual features that will further improve VO‐based motion estimation. The proposed system has been tested extensively in both simulated and real‐world experiments, showing that robust, high‐precision, real‐time localization can be achieved.

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VMVG-Loc: Visual Localization for Autonomous Driving using Vector Map and Voxel Grid Map

Yabuuchi

Kato

2022

2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Monocular localization within sparse voxel maps

Cited by 5 publications

References 17 publications

LiDAR‐based robust localization for field autonomous vehicles in off‐road environments

LiDAR‐based robust localization for field autonomous vehicles in off‐road environments

Multimodal localization: Stereo over LiDAR map

VMVG-Loc: Visual Localization for Autonomous Driving using Vector Map and Voxel Grid Map

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