Algorithmic Solutions for Computing Precise Maximum Likelihood 3D Point Clouds from Mobile Laser Scanning Platforms

Abstract

Mobile laser scanning puts high requirements on the accuracy of the positioning systems and the calibration of the measurement system. We present a novel algorithmic approach for calibration with the goal of improving the measurement accuracy of mobile laser scanners. We describe a general framework for calibrating mobile sensor platforms that estimates all configuration parameters for any arrangement of positioning sensors, including odometry. In addition, we present a novel semi-rigid Simultaneous Localization and Mapping (SLAM) algorithm that corrects the vehicle position at every point in time along its trajectory, while simultaneously improving the quality and precision of the entire acquired point cloud. Using this algorithm, the temporary failure of accurate external positioning systems or the lack thereof can be compensated for. We demonstrate the capabilities of the two newly proposed algorithms on a wide variety of datasets.

Maritime Laser Scanning as the Source for Spatial Data

“…The system consisted of VZ-400 scanner (primarily developed for the purpose of stationary scanning) and multiple frequency and multi-system GNSS Global Navigation Satellite System) receiver connected with IMU (Inertial Measurement Unit), which is responsible for the accurate determination of the parameters of platform movement. Laser scanner (e.g VMZ-400) is a device quite often tested in a mobile solutions [1,2,4,8,12,15,17], as well as for the purpose of scanning from surface of the water [9,15,22,25], but scanning the open sea is still an innovative and problematic solution. VZ-400/VMZ-400 scanner is capable of operating in any inclination which makes it useful under difficult or untypical conditions such as sea conditions.…”

Evaluating Continuous-Time Slam Using a Predefined Trajectory Provided by a Robotic Arm

“…More mathematical details of the algorithm in the available opensource code and are given in (Elseberg et al, 2013). Essentially, we first split the trajectory into sections, and match these sections using the automatic high-precision registration of terrestrial 3D scans, i.e., globally consistent scan matching that is the 6D SLAM core.…”

“…The algorithm is adopted from Elseberg et al (2013), where it was used in a different mobile mapping context, i.e., on wheeled platforms. Unlike other state of the art algorithms, like (Stoyanov and Lilienthal, 2009) and (Bosse et al, 2012), it is not restricted to purely local improvements.…”

“…Here the graph is estimated using a heuristic that measures the overlap of sections using the number of closest point pairs. After applying globally consistent scan matching on the sections the actual continuous-time or semi-rigid matching as described in (Borrmann et al, 2008;Elseberg et al, 2013) is applied, using the results of the rigid optimization as starting values to compute the numerical minimum of the underlying least square problem. To speed up the calculations, we make use of the sparse Cholesky decomposition by (Davis, 2006).…”

Improving Google's Cartographer 3d Mapping by Continuous-Time Slam

“…Our approach optimizes the consistency of the global point cloud, and thus improves on Google's results. We use the algorithms and data from Google as input for our continuous-time SLAM solution, which was recently published in (Elseberg et al, 2013). We present successful applications of the software to our own similar backpack system which delivers consistent 3D point clouds even in absence of an IMU (Nüchter et al, 2015).…”