Automatic coarse co-registration of point clouds from diverse scan geometries: a test of detectors and descriptors

TL;DR

This paper evaluates various keypoint detectors and descriptors for automatic coarse co-registration of diverse point clouds, demonstrating that certain features improve speed without sacrificing accuracy across different sensor geometries.

Contribution

It introduces a benchmarking framework for comparing keypoint detection and description strategies on point clouds from different sensors and geometries, highlighting effective combinations.

Findings

NARF features detect more keypoints and enable faster registration.

Accuracy of co-registration is similar across different detector-descriptor combinations.

Range images effectively simplify geometry for feature matching.

Abstract

Point clouds are collected nowadays from a plethora of sensors, some having higher accuracies and higher costs, some having lower accuracies but also lower costs. Not only there is a large choice for different sensors, but also these can be transported by different platforms, which can provide different scan geometries. In this work we test the extraction of four different keypoint detectors and three feature descriptors. We benchmark performance in terms of calculation time and we assess their performance in terms of accuracy in their ability in coarse automatic co-registration of two clouds that are collected with different sensors, platforms and scan geometries. One, which we define as having the higher accuracy, and thus will be used as reference, was surveyed via a UAV flight with a Riegl MiniVUX-3, the other on a bicycle with a Livox Horizon over a walking path with un-even ground.The novelty in this work consists in comparing several strategies for fast alignment of point clouds from very different surveying geometries, as the drone has a bird's eye view and the bicycle a ground-based view. An added challenge is related to the lower cost of the bicycle sensor ensemble that, together with the rough terrain, reasonably results in lower accuracy of the survey. The main idea is to use range images to capture a simplified version of the geometry of the surveyed area and then find the best features to match keypoints. Results show that NARF features detected more keypoints and resulted in a faster co-registration procedure in this scenariowhereas the accuracy of the co-registration is similar to all the combinations of keypoint detectors and features.