Road Markings Segmentation from LIDAR Point Clouds using Reflectivity Information

Certad, Novel; Morales-Alvarez, Walter; Olaverri-Monreal, Cristina

doi:10.1109/icves56941.2022.9986939

Cited by 3 publications

(4 citation statements)

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“…The drivable area estimation algorithms reliant on reflectivity features could pose a new breakthrough in the state of the art. The authors in [40] introduce a technique for lane marking estimation based on lidar reflectivity that could be adapted for drivable area estimation. • Low-fidelity map fusion: Open-source low-fidelity maps offer valuable context information for drivable area estimation algorithms that is currently underrepresented in the state of the art.…”

Section: Discussion-future Researchmentioning

confidence: 99%

“…Road Markings [40] implement coarse ground segmentation through RANSAC plane fitting, along with regional grow-clustering to weed out points belonging to the curb. Then, they apply adaptive thresholding based on Otsu's method [54] on the reflectivity information coming from the sensor.…”

Section: Multi-cue [38]mentioning

confidence: 99%

“…Learning-based methods usually offer the higher performance [26][27][28]30,36]. However, there are non-learning-based methods that reach similar performance levels [35,40,41].…”

mentioning

confidence: 99%

“…LiDAR or RGB-D + RGB) pays off in terms of performance. Nevertheless it is not a must as publications with a single sensor input also perform reasonably well [30,36,40].…”

mentioning

confidence: 99%

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Recent Developments on Drivable Area Estimation: A Survey and a Functional Analysis

Hortelano,

Villagrá,

Godoy

et al. 2023

Sensors

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Most advanced autonomous driving systems (ADS) today rely on the prior creation of high-definition maps (HD maps). This process is expensive and needs to be performed frequently to keep up with the changing conditions of the road environment. Creating accurate navigation maps online is an alternative to reduce the cost and broaden the current operational design domains (ODD) of modern ADS. This paper offers a snapshot of the state of the art in drivable area estimation, which is an essential technology to deploy ADS in ODDs where HD maps are limited or unavailable. The proposed review introduces a novel architecture breakdown that fits learning-based and non-learning-based techniques and allows the analysis of a set of impactful and recent drivable area algorithms. In addition to that, complimentary information for practitioners is provided: (i) an assessment of the influence of modern sensing technologies on the task under study and (ii) a selection of relevant datasets for evaluation and benchmarking purposes.

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Section: Discussion-future Researchmentioning

confidence: 99%

Section: Multi-cue [38]mentioning

confidence: 99%

“…Learning-based methods usually offer the higher performance [26][27][28]30,36]. However, there are non-learning-based methods that reach similar performance levels [35,40,41].…”

mentioning

confidence: 99%

“…LiDAR or RGB-D + RGB) pays off in terms of performance. Nevertheless it is not a must as publications with a single sensor input also perform reasonably well [30,36,40].…”

mentioning

confidence: 99%

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