English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

This paper addresses the registration of LiDAR point clouds. More specifically, we present an automatic method for markerless\udregistration of two such point clouds given in arbitrary local scan coordinates – i.e. without simplifying assumptions such as a common\udup-vector. Clearly, the critical step of the registration is to find a coarse initial alignment, to be refined with established local methods for\udfine registration, such as ICP or least-squares surface matching. The proposed approach builds on the <i>4-Points Congruent Sets</i> (4PCS)\udalgorithm (Aiger et al., 2008), a popular registration tool in computer graphics, and extends it to better deal with the specific challenges\udof LiDAR data. The main limitations of the original 4PCS method in that context are (i) that it does not cope well with strongly varying\udpoint densities, such as they routinely occur in laser scans due to the constant angular sampling from different viewpoints; and (ii) that\udto remain efficient, huge LiDAR point clouds must be down-sampled so heavily that approximate point-to-point correspondence can no\udlonger be guaranteed. To overcome these drawbacks we propose not to apply 4PCS to the original point cloud (respectively, a randomly\udor regularly subsampled version of it), but rather to represent the point clouds with sets of distinctive 3D keypoints, and run (a slightly\udmodified) 4PCS on the keypoint sets. The resulting combination, termed <i>Keypoint-based 4-Points Congruent Sets</i> (K-4PCS), proves to\udbe very reliable: with suitable parameter settings, tests in indoor as well as outdoor environments yield 100% success rates

Markerless point cloud registration with keypoint-based 4-points congruent sets