Range Image-based LiDAR Localization for Autonomous Vehicles

Chen, Xieyuanli; Vizzo, Ignacio; Läbe, Thomas; Behley, Jens; Stachniss, Cyrill

doi:10.1109/icra48506.2021.9561335

Cited by 85 publications

(41 citation statements)

References 25 publications

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“…Traditional approaches to robot localization rely on probabilistic state estimation techniques. A popular framework is Monte Carlo localization [16], which uses a particle filter to estimate the pose of the robot and is still widely used in robot localization systems [4,9,13,22,36,40,47].…”

Section: Related Workmentioning

confidence: 99%

“…The key idea of our approach is to use range images generated from LiDAR scans for pole extraction. Following the prior work [12,13], we utilize a spherical projection for range images generation. Each LiDAR point = ( , , ) is mapped to spherical coordinates via a mapping Π ∶ ℝ 3 ↦ ℝ 2 and finally to image coordinates, as defined by…”

Section: Range Image Generationmentioning

confidence: 99%

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Online Pole Segmentation on Range Images for Long-term LiDAR Localization in Urban Environments

Dong¹,

Chen²,

Särkkä³

et al. 2022

Preprint

Self Cite

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Robust and accurate localization is a basic requirement for mobile autonomous systems. Pole-like objects, such as traffic signs, poles, and lamps are frequently used landmarks for localization in urban environments due to their local distinctiveness and long-term stability. In this paper, we present a novel, accurate, and fast pole extraction approach based on geometric features that runs online and has little computational demands. Our method performs all computations directly on range images generated from 3D LiDAR scans, which avoids processing 3D point clouds explicitly and enables fast pole extraction for each scan. We further use the extracted poles as pseudo labels to train a deep neural network for online range image-based pole segmentation. We test both our geometric and learningbased pole extraction methods for localization on different datasets with different LiDAR scanners, routes, and seasonal changes. The experimental results show that our methods outperform other stateof-the-art approaches. Moreover, boosted with pseudo pole labels extracted from multiple datasets, our learning-based method can run across different datasets and achieve even better localization results compared to our geometry-based method. We released our pole datasets to the public for evaluating the performance of pole extractors, as well as the implementation of our approach.

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Section: Related Workmentioning

confidence: 99%

Section: Range Image Generationmentioning

confidence: 99%

Online Pole Segmentation on Range Images for Long-term LiDAR Localization in Urban Environments

Dong¹,

Chen²,

Särkkä³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Another example application where localization is required are autonomous VOLUME 4, 2016 vehicles. Using a localization system, autonomous car or a mobile robot is capable to estimate its pose in a map based on on-board sensors information [10]. In smart cities and smart buildings, knowing the location of a user and/or device paves the way for many new applications like public safety, tracking services and robot guidance (in-building) [11].…”

Section: Introductionmentioning

confidence: 99%

Constrained Localization: A Survey

Savić¹,

Vilajosana

Watteyne³

2022

IEEE Access

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Indoor localization techniques have been extensively studied in the last decade. The well-established technologies enable the development of Real-Time Location Systems (RTLS). A good body of publications emerged, with several survey papers that provide a deep analysis of the research advances. Existing survey papers focus on either a specific technique and technology or on a general overview of indoor localization research. However, there is a need for a use case-driven survey on both recent academic research and commercial trends, as well as a hands-on evaluation of commercial solutions. This work aims at helping researchers select the appropriate technology and technique suitable for developing low-cost, low-power localization system, capable of providing centimeter level accuracy. The article is both a survey on recent academic research and a hands-on evaluation of commercial solutions. We introduce a specific use case as a guiding application throughout this article: localizing low-cost low-power miniature wireless swarm robots. We define a taxonomy and classify academic research according to five criteria: Line of Sight (LoS) requirement, accuracy, update rate, battery life, cost. We discuss localization fundamentals, the different technologies and techniques, as well as recent commercial developments and trends. Besides the traditional taxonomy and survey, this article also presents a hands-on evaluation of popular commercial localization solutions based on Bluetooth Angle of Arrival (AoA) and Ultra-Wideband (UWB). We conclude this article by discussing the five most important open research challenges: lightweight filtering algorithms, zero infrastructure dependency, low-power operation, security, and standardization.

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“…Light detection and ranging (LiDAR) pointclouds are vital for 3D perception and geometrical understanding of a surrounding environment for autonomous vehicles. Numerous tasks are dependent on 3D information from lidar, such as 3D object detection [1][2][3][4][5], 3D semantic segmentation [6], localization [7], mapping [8] and path planning [9]. Mounted on the vehicle, lidar rotates around a vertical axis and emits pulses of infrared light waves to retrieve accurate 3D measurements.…”

Section: Introductionmentioning

confidence: 99%

GLPU: A Geometric Approach For Lidar Pointcloud Upsampling

Eskandar¹,

Palaniswamy²,

Guirguis³

et al. 2022

Preprint

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In autonomous driving, lidar is inherent for the understanding of the 3D environment. Lidar sensors vary in vertical resolutions, where a denser pointcloud depicts a more detailed environment, albeit at a significantly higher cost. Pointcloud upsampling predicts high-resolution pointclouds from sparser ones to bridge this performance gap at a lower cost. Although many upsampling frameworks have achieved a robust performance, a fair comparison is difficult as they were tested on different datasets and metrics. In this work, we first conduct a consistent comparative study to benchmark the existing algorithms on the KITTI dataset. Then, we observe that there are three common factors that hinder the performance: an inefficient data representation, a small receptive field, and low-frequency losses. By leveraging the scene geometry, a new self-supervised geometric lidar pointcloud upsampling (GLPU) framework is proposed to address the aforementioned limitations. Our experiments demonstrate the effectiveness and superior performance of GLPU compared to other techniques on the KITTI benchmark.

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Range Image-based LiDAR Localization for Autonomous Vehicles

Cited by 85 publications

References 25 publications

Online Pole Segmentation on Range Images for Long-term LiDAR Localization in Urban Environments

Online Pole Segmentation on Range Images for Long-term LiDAR Localization in Urban Environments

Constrained Localization: A Survey

GLPU: A Geometric Approach For Lidar Pointcloud Upsampling

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