LiDAR-based Drivable Region Detection for Autonomous Driving

Xue, Hanzhang; Fu, Hao; Ren, Ruike; Zhang, Jintao; Liu, Bo‐Kai; Fan, Yiming; Dai, Bin

doi:10.1109/iros51168.2021.9636289

Cited by 16 publications

(6 citation statements)

References 23 publications

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“…Two fusion strategies could be adopted here: the first one is a Kalman filter based approach that we proposed in our previous work (Xue et al, 2021). In this approach, the observed mean elevation

{<mpadded xmlns="http://www.w3.org/1998/Math/MathML">\mu </mpadded>}_{i}^{t}

is used to recursively update the joint elevation distribution

\hat{{\mathscr{N}}}({<mpadded xmlns="http://www.w3.org/1998/Math/MathML">\hat{\mu }</mpadded>}_{i}^{t},{<mpadded xmlns="http://www.w3.org/1998/Math/MathML">\hat{{\rm{\Sigma }}}</mpadded>}_{i}^{t})

$\left\{\begin{array}{lll}{<mpadded xmlns="http://www.w3.org/1998/Math/MathML">\bar{\mu }</mpadded>}_{i}^{t} & = & a{<mpadded xmlns="http://www.w3.org/1998/Math/MathML">\hat{\mu }</mpadded>}_{i}^{t-1},\\ {<mpadded xmlns="http://www.w3.org/1998/Ma...

…”

Section: Methodsmentioning

confidence: 99%

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Traversability analysis for autonomous driving in complex environment: A LiDAR‐based terrain modeling approach

Xue

Liang³

et al. 2023

Journal of Field Robotics

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For autonomous driving, traversability analysis is one of the most basic and essential tasks. In this paper, we propose a novel LiDAR‐based terrain modeling approach, which could output stable, complete, and accurate terrain models and traversability analysis results. As terrain is an inherent property of the environment that does not change with different view angles, our approach adopts a multiframe information fusion strategy for terrain modeling. Specifically, a normal distributions transform mapping approach is adopted to accurately model the terrain by fusing information from consecutive LiDAR frames. Then the spatial‐temporal Bayesian generalized kernel inference and bilateral filtering are utilized to promote the stability and completeness of the results while simultaneously retaining the sharp terrain edges. Based on the terrain modeling results, the traversability of each region is obtained by performing geometric connectivity analysis between neighboring terrain regions. Experimental results show that the proposed method could run in real‐time and outperforms state‐of‐the‐art approaches.

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{<mpadded xmlns="http://www.w3.org/1998/Math/MathML">\mu </mpadded>}_{i}^{t}

is used to recursively update the joint elevation distribution

\hat{{\mathscr{N}}}({<mpadded xmlns="http://www.w3.org/1998/Math/MathML">\hat{\mu }</mpadded>}_{i}^{t},{<mpadded xmlns="http://www.w3.org/1998/Math/MathML">\hat{{\rm{\Sigma }}}</mpadded>}_{i}^{t})

$\left\{\begin{array}{lll}{<mpadded xmlns="http://www.w3.org/1998/Math/MathML">\bar{\mu }</mpadded>}_{i}^{t} & = & a{<mpadded xmlns="http://www.w3.org/1998/Math/MathML">\hat{\mu }</mpadded>}_{i}^{t-1},\\ {<mpadded xmlns="http://www.w3.org/1998/Ma...

…”

Section: Methodsmentioning

confidence: 99%

Section: Elevation Estimation Based On Multiframe Information Fusionmentioning

confidence: 99%

Traversability analysis for autonomous driving in complex environment: A LiDAR‐based terrain modeling approach

Xue

Liang³

et al. 2023

Journal of Field Robotics

Self Cite

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show abstract

“…LiDAR sensors can assist in detecting the road and the drivable area, where high-level algorithms are able to accurately identify road boundaries, markings, lanes, and curbs, aiding in a correct evaluation of the road and ensuring efficient navigation of the vehicle [17][18][19]. To better perform these tasks, a ground segmentation step can be applied to the point cloud data [20], which enhances the subsequent identification of environmental features.…”

Section: Drivable Area Detectionmentioning

confidence: 99%

A Survey on Data Compression Techniques for Automotive LiDAR Point Clouds

Roriz,

Silva,

Dias

et al. 2024

Sensors

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In the evolving landscape of autonomous driving technology, Light Detection and Ranging (LiDAR) sensors have emerged as a pivotal instrument for enhancing environmental perception. They can offer precise, high-resolution, real-time 3D representations around a vehicle, and the ability for long-range measurements under low-light conditions. However, these advantages come at the cost of the large volume of data generated by the sensor, leading to several challenges in transmission, processing, and storage operations, which can be currently mitigated by employing data compression techniques to the point cloud. This article presents a survey of existing methods used to compress point cloud data for automotive LiDAR sensors. It presents a comprehensive taxonomy that categorizes these approaches into four main groups, comparing and discussing them across several important metrics.

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“…For machine learning algorithms, features such as the RGB color, Walsh Hadamard, Histogram of Oriented Gradients (HOG), Local Binary Pattern (LBP), Haar, and LUV channel, can be extracted by the feature extractors and the classification header, such as Support Vector Machine (SVM), Conditional Random Field (CRF), to obtain the final results. The deep neural network can replace the feature extractors and some improvements, such as employing the large visual regions convolutional kernels [44], connection by multiple layers [45], to achieve competitive performance. We found that learning-based driving region detection results are usually one of the branches of the scene understanding task and researchers attempt to tackle a few challenges including 2D-3D transformation, complex driving regions, etc.…”

Section: B Object Detectionmentioning

confidence: 99%

Milestones in Autonomous Driving and Intelligent Vehicles: Survey of Surveys

Chen

Huang

et al. 2023

IEEE Trans. Intell. Veh.

248

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Interest in autonomous driving (AD) and intelligent vehicles (IVs) is growing at a rapid pace due to the convenience, safety, and economic benefits. Although a number of surveys have reviewed research achievements in this field, they are still limited in specific tasks, lack of systematic summary and research directions in the future. Here we propose a Survey of Surveys (SoS) for total technologies of AD and IVs that reviews the history, summarizes the milestones, and provides the perspectives, ethics, and future research directions. To our knowledge, this article is the first SoS with milestones in AD and IVs, which constitutes our complete research work together with two other technical surveys. We anticipate that this article will bring novel and diverse insights to researchers and abecedarians, and serve as a bridge between past and future.

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LiDAR-based Drivable Region Detection for Autonomous Driving

Cited by 16 publications

References 23 publications

Traversability analysis for autonomous driving in complex environment: A LiDAR‐based terrain modeling approach

Traversability analysis for autonomous driving in complex environment: A LiDAR‐based terrain modeling approach

A Survey on Data Compression Techniques for Automotive LiDAR Point Clouds

Milestones in Autonomous Driving and Intelligent Vehicles: Survey of Surveys

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