Efficient Continuous-Time SLAM for 3D Lidar-Based Online Mapping

Modern 3D laser-range scanners have a high data rate, making online simultaneous localization and mapping (SLAM) computationally challenging. Recursive state estimation techniques are efficient but commit to a state estimate immediately after a new scan is made, which may lead to misalignments of measurements. We present a 3D SLAM approach that allows for refining alignments during online mapping. Our method is based on efficient local mapping and a hierarchical optimization back-end. Measurements of a 3D laser scanner are aggregated in local multiresolution maps by means of surfel-based registration. The local maps are used in a multi-level graph for allocentric mapping and localization. In order to incorporate corrections when refining the alignment, the individual 3D scans in the local map are modeled as a sub-graph and graph optimization is performed to account for drift and misalignments in the local maps. Furthermore, in each sub-graph, a continuous-time representation of the sensor trajectory allows to correct measurements between scan poses. We evaluate our approach in multiple experiments by showing qualitative results. Furthermore, we quantify the map quality by an entropy-based measure.Comment: In: Proceedings of the International Conference on Robotics and Automation (ICRA) 201

A sensor skid for precise 3d modeling of production lines

ABSTRACT: Motivated by the increasing need of rapid characterization of environments in 3D, we designed and built a sensor skid that automates the work of an operator of terrestrial laser scanners. The system combines terrestrial laser scanning with kinematic laser scanning and uses a novel semi-rigid SLAM method. It enables us to digitize factory environments without the need to stop production. The acquired 3D point clouds are precise and suitable to detect objects that collide with items moved along the production line

Conceptual issues regarding the development of underground railway laser scanning systems

Author name used in this publication: Bruce King2014-2015 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

Koordinacija više robota za učinkovite pretraživanje prostora

This paper addresses the problem of exploration of an unknown environment by developing effective exploration strategies for a team of mobile robots equipped with continuously rotating 3D scanners. The main aim of the new strategies is to reduce the exploration time of unknown environment. Unlike most of other published works, to save time, the laser scanners rotate and scan the environment while robots are in motion. Furthermore, the new strategies are able to explore large outdoor environments as a considerable reduction of the required computations, especially those required for path planning, have been achieved. Moreover, another new exploration strategy has been developed so that robots continuously replan the order to visit the remaining unexplored areas according to the new data (i.e. updated map) collected by the robot in question or by the other team members. This new extension led to further enhancements over the above mentioned ones, but with slightly higher computational costs. Finally, to assess our new exploration strategies with different levels of environment complexity, new set of experiments were conducted in environments where obstacles are distributed according to the Hilbert curve. The results of these experiments show the effectiveness of the proposed technique to effectively distribute the robots over the environment. More importantly, we show how the optimal number of robots is related to the environment complexity.Ovaj članak istražuje problem pretraživanja nepoznatog prostora razvijanjem učinkovite strategije za tim mobilnih robota s rotirajućim 3D laserskim senzorom. Glavni cilj ove nove strategije je smanjenje vremena pretraživanja nepoznatog prostora. Za razliku od većine objavljenih radova, u ovome članku, radi smanjenja vremena, laserski senzori rotiraju i snimaju prostor dok su roboti još u pokretu. Predložene strategije, pošto se njima znatno smanjuje računska složenost, pogotovo za planiranje gibanja, omogućuju pretraživanje i vanjskih prostora prostora velikih dimenzija. Nadalje, razvijena je još jedna strategija pretraživanja koja omogućuje robotima da kontinuirano replaniraju poredak kojim će posjetiti ostatak neistraženog prostora, prema novim podacima (ažuriranoj karti) prikupljenim od njih samih ili drugih članova tima. Ovo novo proširenje nadalje unaprjeđuje performanse algoritma, ali uz nešto veću računsku složenost. Kako bi se u konačnici testirale nove strategije pretraživanja na prostorima različite složenosti, provedeni su eksperimenti s preprekama raspoređenim po Hilbertovoj krivulji. Rezultati eksperimenata pokazuju učinkovitost predložene metode u prostornom raspoređivanju robota. Od posebne je važnosti istaknuti da se u članku također istražuje odnos između broja robota i kompleksnosti prostora

Maximum likelihood point cloud acquisition from a mobile platform

This paper describes an approach to acquire locally consistent range data scans from a moving sensor platform. Data from a vertically mounted rotating laser scanner and odometry position estimates are fused and used to estimate maximum likelihood point clouds. An estimation algorithm is applied to reduce the accumulated error after a full rotation of the range finder. A configuration consisting of a SICK laser scanner mounted on a rotational actuator is described and used to evaluate the proposed approach. The data sets analyzed suggest a significant improvement in point cloud consistency, even over a short travel distance