robots-11

Outdoor Localization

3260 papers • 126 benchmarks • 313 datasets

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Most implemented papers

Panoramic Annular Localizer: Tackling the Variation Challenges of Outdoor Localization Using Panoramic Annular Images and Active Deep Descriptors

Kaiwei Wang, Kailun Yang, Dongming Sun, Ruiqi Cheng, Shufei Lin, Weijian Hu, Xiao Huang, Huabing Li, J. Bai•Mon May 13 2019

Panoramic Annular Localizer into which panoramic annular lens and robust deep image descriptors are incorporated is proposed in this paper and the experiments carried on the public datasets and in the field illustrate the validation of the proposed system.

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Real-Time Outdoor Localization Using Radio Maps: A Deep Learning Approach

R. Levie, cCaugkan Yapar, Gitta Kutyniok, G. Caire•Tue Jun 22 2021

A convolutional, end-to-end trained neural network for the localization task, which is able to estimate the position of a user from the received signal strength of a small number of Base Stations, which can localize users with state-of-the-art accuracy and enjoys high robustness to inaccuracies in the estimations of radio maps.

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SGLoc: Scene Geometry Encoding for Outdoor LiDAR Localization

Chenglu Wen, Wen Li, Shangshu Yu, Cheng Wang, Guosheng Hu, Siqi Shen•Wed May 31 2023

LiDAR-based absolute pose regression estimates the global pose through a deep network in an end-to-end manner, achieving impressive results in learning-based localization. However, the accuracy of existing methods still has room to improve due to the difficulty of effectively encoding the scene geometry and the unsatisfactory quality of the data. In this work, we propose a novel LiDAR localization frame-work, SGLoc, which decouples the pose estimation to point cloud correspondence regression and pose estimation via this correspondence. This decoupling effectively encodes the scene geometry because the decoupled correspondence regression step greatly preserves the scene geometry, leading to significant performance improvement. Apart from this decoupling, we also design a tri-scale spatial feature aggregation module and inter-geometric consistency constraint loss to effectively capture scene geometry. Moreover, we empirically find that the ground truth might be noisy due to GPS/INS measuring errors, greatly reducing the pose estimation performance. Thus, we propose a pose quality evaluation and enhancement method to measure and correct the ground truth pose. Extensive experiments on the Oxford Radar RobotCar and NCLT datasets demonstrate the effectiveness of SGLoc, which outperforms state-of-the-art regression-based localization methods by 68.5% and 67.6% on position accuracy, respectively.

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Representing 3D sparse map points and lines for camera relocalization

Thuan Bui Bach, D. Tran, Joo-Ho Lee, Huy-Hoang Bui•Tue Feb 27 2024

This study shows how a lightweight neural network can learn to represent both 3D point and line features, and exhibit leading pose accuracy by harnessing the power of multiple learned mappings.

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