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Grant support

This work was supported in part by the Spanish Ministry of Science and Innovation through the National Project Programmable Systems for Intelligence in Automobiles (PRYSTINE) under Grant PCI2018-092928 and the National Project Product Security for Cross Domain Reliable Dependable Automated Systems (SECREDAS) under Grant PCI2018-093144, in part by the Community of Madrid through SEGVAUTO 4.0-CM Programme under Grant S2018-EMT-4362, and in part by the European Commission and Electronic Components and Systems for European Leadership (ECSEL) Joint Undertaking through the Project PRYSTINE under Grant 783190 and the Project SECREDAS under Grant 783119.

Analysis of institutional authors

Jimenez, VCorresponding AuthorGodoy, JAuthorVillagra, JAuthor

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October 18, 2021
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Ground Segmentation Algorithm for Sloped Terrain and Sparse LiDAR Point Cloud

Publicated to:Ieee Access. 9 132914-132927 - 2021-01-01 9(), DOI: 10.1109/ACCESS.2021.3115664

Authors: Jimenez, V; Godoy, J; Artunedo, A; Villagra, J

Affiliations

Ctr Automat & Robot CSIC UPM, Madrid 28500, Spain - Author

Abstract

Distinguishing obstacles from ground is an essential step for common perception tasks such as object detection-and-tracking or occupancy grid maps. Typical approaches rely on plane fitting or local geometric features, but their performance is reduced in situations with sloped terrain or sparse data. Some works address these issues using Markov Random Fields and Belief Propagation, but these rely on local geometric features uniquely. This article presents a strategy for ground segmentation in LiDAR point clouds composed by two main steps: (i) First, an initial classification is performed dividing the points in small groups and analyzing geometric features between them. (ii) Then, this initial classification is used to model the surrounding ground height as a Markov Random Field, which is solved using the Loopy Belief Propagation algorithm. Points are finally classified comparing their height with the estimated ground height map. On one hand, using an initial estimation to model the Markov Random Field provides a better description of the scene than local geometric features commonly used alone. On the other hand, using a graph-based approach with message passing achieves better results than simpler filtering or enhancement techniques, since data propagation compensates sparse distributions of LiDAR point clouds. Experiments are conducted with two different sources of information: nuScenes's public dataset and an autonomous vehicle prototype. The estimation results are analyzed with respect to other methods, showing a good performance in a variety of situations.

Keywords

Autonomous vehiclesBelief propagationChannel-basedClassification algorithmsEstimationGeometric featureGeometric featuresGeometryGraphic methodsImage segmentationInference algorithmsLandformsLaser radarLidarMarkov processesMarkov random fieldMarkov random fieldsMessage passingObject detectionObstacle-ground segmentationOptical radarPerformancePoint-cloudsSloped terrainSloped terrainsSparse point cloudSurfaceTask analysis

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Ieee Access due to its progression and the good impact it has achieved in recent years, according to the agency Scopus (SJR), it has become a reference in its field. In the year of publication of the work, 2021, it was in position , thus managing to position itself as a Q1 (Primer Cuartil), in the category Engineering (Miscellaneous).

From a relative perspective, and based on the normalized impact indicator calculated from World Citations from Scopus Elsevier, it yields a value for the Field-Weighted Citation Impact from the Scopus agency: 1.16, which indicates that, compared to works in the same discipline and in the same year of publication, it ranks as a work cited above average. (source consulted: ESI Nov 14, 2024)

This information is reinforced by other indicators of the same type, which, although dynamic over time and dependent on the set of average global citations at the time of their calculation, consistently position the work at some point among the top 50% most cited in its field:

  • Field Citation Ratio (FCR) from Dimensions: 4.9 (source consulted: Dimensions Jul 2025)

Specifically, and according to different indexing agencies, this work has accumulated citations as of 2025-07-05, the following number of citations:

  • WoS: 1
  • Scopus: 17
  • Google Scholar: 19

Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2025-07-05:

  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 41 (PlumX).

It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

  • The work has been submitted to a journal whose editorial policy allows open Open Access publication.

Leadership analysis of institutional authors

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (JIMÉNEZ BERMEJO, VÍCTOR) and Last Author (Villagrá Serrano, Jorge).

the author responsible for correspondence tasks has been JIMÉNEZ BERMEJO, VÍCTOR.