Segmentation of 3D lidar data in non-flat urban environments using a local convexity criterion

Moosmann, Frank; Pink, Oliver; Stiller, Christoph

doi:10.1109/ivs.2009.5164280

Cited by 264 publications

(176 citation statements)

References 20 publications

(20 reference statements)

Supporting

Mentioning

171

Contrasting

Order By: Relevance

“…This may sometimes lead to erroneous segmentation. Also common in such type of methods is a node based approach [5] in which at every node, boundary conditions have to be checked in all 5 different possible directions. In our work, we have proposed a link-chain method instead to group these s-voxels together into segmented objects.…”

Section: Clustering By Link-chain Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Segmentation Based Classification of 3D Urban Point Clouds: A Super-Voxel Based Approach with Evaluation

2013

View full text Add to dashboard Cite

Segmentation and classification of urban range data into different object classes have several challenges due to certain properties of the data, such as density variation, inconsistencies due to missing data and the large data size that require heavy computation and large memory. A method to classify urban scenes based on a super-voxel segmentation of sparse 3D data obtained from LiDAR sensors is presented. The 3D point cloud is first segmented into voxels, which are then characterized by several attributes transforming them into super-voxels. These are joined together by using a link-chain method rather than the usual region growing algorithm to create objects. These objects are then classified using geometrical models and local descriptors. In order to evaluate the results, a new metric that combines both segmentation and classification results simultaneously is presented. The effects of voxel size and incorporation of RGB color and laser reflectance intensity on the classification results are also discussed. The method is evaluated on standard data sets using different metrics to demonstrate its efficacy.

show abstract

Section: Clustering By Link-chain Methodsmentioning

confidence: 99%

“…In literature survey, we also find some segmentation methods based on surface discontinuities such as Moosman et al [5], who used surface convexity in a terrain mesh as a separator between objects.…”

Section: Specialized Features and Surface Discontinuitiesmentioning

confidence: 99%

Segmentation Based Classification of 3D Urban Point Clouds: A Super-Voxel Based Approach with Evaluation

2013

View full text Add to dashboard Cite

show abstract

“…In contrast to depth sensors, LiDAR devices scan the environment and obtain the 3D points one-by-one. Based on the features of scanlines, many methods have been proposed for different purposes, such as people detection, segmentation and point cloud registration [11][12][13][14][15].…”

Section: Related Workmentioning

confidence: 99%

Incremental and Enhanced Scanline-Based Segmentation Method for Surface Reconstruction of Sparse LiDAR Data

2016

View full text Add to dashboard Cite

Abstract:The segmentation of point clouds is an important aspect of automated processing tasks such as semantic extraction. However, the sparsity and non-uniformity of the point clouds gathered by the popular 3D mobile LiDAR devices pose many challenges for existing segmentation methods. To improve the segmentation results of point clouds from mobile LiDAR devices, we propose an optimized segmentation method based on Scanline Continuity Constraint (SLCC) in this work. Unlike conventional scanline-based segmentation methods, SLCC clusters scanlines using the continuity constraints in terms of the distance as well as the direction of two consecutive points. In addition, scanline clusters are agglomerated not only into primitive geometrical shapes but also irregular shapes. Another downside to existing segmentation methods is that they are not capable of incremental processing. This causes unnecessary memory and time consumption for applications that require frame-wise segmentation or when new point clouds are added. In order to address this, we propose an incremental scheme-the Incremental Recursive Segmentation (IRIS), that can be easily applied to any segmentation method. IRIS is achieved by combining the segments of newly added point clouds and the previously segmented results. Furthermore, as an example application, we construct a processing pipeline consisting of plane fitting and surface reconstruction using the segmentation results. Finally, we evaluate the proposed methods on three datasets acquired from a handheld Velodyne HDL-32E LiDAR device. The experimental results verify the efficiency of IRIS for any segmentation method and the advantages of SLCC for processing mobile LiDAR data.

show abstract

“…This paper proposes to solve data association by reasoning at the object level rather than at the point-to-point level. Recently proposed segmentation techniques [10], [19] allow the separation of objects in 3D scans. Using such segmentation as an input, the proposed methods explicitly match objects (i.e.…”

Section: Introductionmentioning

confidence: 99%

“…The novelty of the proposed approach is in the segment matching process which takes into account the proximity of segments, their shape, and the consistency of their relative locations in each scan. Scan segmentation is here assumed to be given (recent studies provide various alternatives [10], [19]). The method is tested on seven sequences of Velodyne scans acquired in urban environments.…”

mentioning

confidence: 99%

Scan segments matching for pairwise 3D alignment

Douillard

Quadros

Morton

et al. 2012

2012 IEEE International Conference on Robotics and Automation

View full text Add to dashboard Cite

Abstract-This paper presents a method for pairwise 3D alignment which solves data association by matching scan segments across scans. Generating accurate segment associations allows to run a modified version of the Iterative Closest Point (ICP) algorithm where the search for point-to-point correspondences is constrained to associated segments. The novelty of the proposed approach is in the segment matching process which takes into account the proximity of segments, their shape, and the consistency of their relative locations in each scan. Scan segmentation is here assumed to be given (recent studies provide various alternatives [10], [19]). The method is tested on seven sequences of Velodyne scans acquired in urban environments. Unlike various other standard versions of ICP, which fail to recover correct alignment when the displacement between scans increases, the proposed method is shown to be robust to displacements of several meters. In addition, it is shown to lead to savings in computational times which are potentially critical in real-time applications.

show abstract

Segmentation of 3D lidar data in non-flat urban environments using a local convexity criterion

Cited by 264 publications

References 20 publications

Segmentation Based Classification of 3D Urban Point Clouds: A Super-Voxel Based Approach with Evaluation

Segmentation Based Classification of 3D Urban Point Clouds: A Super-Voxel Based Approach with Evaluation

Incremental and Enhanced Scanline-Based Segmentation Method for Surface Reconstruction of Sparse LiDAR Data

Scan segments matching for pairwise 3D alignment

Contact Info

Product

Resources

About