WEIGHTED ICP POINT CLOUDS REGISTRATION BY SEGMENTATION BASED ON EIGENFEATURES CLUSTERING

Dense point clouds can be nowadays considered the main product of UAV (Unmanned Aerial Vehicle) photogrammetric processing and clouds registration is still a key aspect in case of blocks acquired apart. In the paper some overlapping datasets, acquired with a multispectral Parrot Sequoia camera above some rice fields, are analysed in a single block approach. Since the sensors is equipped with a navigation-grade sensor, the georeferencing information is affected by large errors and the so obtained dense point clouds are significantly far apart: to register them the Iterative Closes Point (ICP) technique is applied. ICP convergence is fundamentally based on the correct selection of the points to be coupled, and the paper proposes an innovative procedure in which a double density points subset is selected in relation to terrain characteristics. This approach reduces the complexity of the calculation and avoids that flat terrain parts, where most of the original points, are de-facto overweighed. Starting from the original dense cloud, eigenfeatures are extracted for each point and clustering is then performed to group them in two classes connected to terrain geometry, flat terrain or not; two metrics are adopted and compared for k-means clustering, Euclidean and City Block. Segmentation results are evaluated visually and by comparison with manually performed classification; ICP are then performed and the quality of registration is assessed too. The presented results show how the proposed procedure seem capable to register clouds even far apart with a good overall accuracy.

Use of a Weighted ICP Algorithm to Precisely Determine USV Movement Parameters

The purpose of this article is to present a study aimed at developing a method for the precise determination of unmanned surface vehicle (USV) movement parameters (heading (HDG), speed over ground (SOG) and rate of turn (ROT)) through appropriate processing. The technique employs a modified weighted ICP (Iterative Closest Point) algorithm and a 2D points layer arranged in the horizon plane obtained from measurements. This is performed with the help of Light Detection and Ranging (LIDAR). A new method of weighting is presented. It is based on a mean error in a given direction and the results of modified weighted ICP tests carried out on the basis of field measurement data. The first part of the paper characterizes LIDAR measuring errors and indicates the possibilities for their use in matching point clouds. The second part of the article deals with a method for determining the SOG and course over ground (COG), based on a modified weighted ICP algorithm. The main part of the paper reviews a test method aimed at evaluating the accuracy of determining the SOG and COG by the scan-matching method using a modified weighted ICP algorithm. The final part presents an analysis comparing the obtained SOG and COG results with reference results of GNSS RTK measurements and the resulting generalised conclusions.