TY  - JOUR
A1  - Lauterbach, Helge A.
A1  - Borrmann, Dorit
A1  - Heß, Robin
A1  - Eck, Daniel
A1  - Schilling, Klaus
A1  - Nüchter, Andreas
T1  - Evaluation of a Backpack-Mounted 3D Mobile Scanning System
JF  - Remote Sensing
N2  - Recently, several backpack-mounted systems, also known as personal laser scanning systems, have been developed. They consist of laser scanners or cameras that are carried by a human operator to acquire measurements of the environment while walking. These systems were first designed to overcome the challenges of mapping indoor environments with doors and stairs. While the human operator inherently has the ability to open doors and to climb stairs, the flexible movements introduce irregularities of the trajectory to the system. To compete with other mapping systems, the accuracy of these systems has to be evaluated. In this paper, we present an extensive evaluation of our backpack mobile mapping system in indoor environments. It is shown that the system can deal with the normal human walking motion, but has problems with irregular jittering. Moreover, we demonstrate the applicability of the backpack in a suitable urban scenario.
KW  - man-portable mapping
KW  - backpack mobile mapping
KW  - SLAM
KW  - mobile laser scanning
KW  - personal laser scanning
Y1  - 2015
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-126247
VL  - 7
IS  - 10
ER  - 
TY  - JOUR
A1  - Du, Shitong
A1  - Lauterbach, Helge A.
A1  - Li, Xuyou
A1  - Demisse, Girum G.
A1  - Borrmann, Dorit
A1  - Nüchter, Andreas
T1  - Curvefusion — A Method for Combining Estimated Trajectories with Applications to SLAM and Time-Calibration
JF  - Sensors
N2  - Mapping and localization of mobile robots in an unknown environment are essential for most high-level operations like autonomous navigation or exploration. This paper presents a novel approach for combining estimated trajectories, namely curvefusion. The robot used in the experiments is equipped with a horizontally mounted 2D profiler, a constantly spinning 3D laser scanner and a GPS module. The proposed algorithm first combines trajectories from different sensors to optimize poses of the planar three degrees of freedom (DoF) trajectory, which is then fed into continuous-time simultaneous localization and mapping (SLAM) to further improve the trajectory. While state-of-the-art multi-sensor fusion methods mainly focus on probabilistic methods, our approach instead adopts a deformation-based method to optimize poses. To this end, a similarity metric for curved shapes is introduced into the robotics community to fuse the estimated trajectories. Additionally, a shape-based point correspondence estimation method is applied to the multi-sensor time calibration. Experiments show that the proposed fusion method can achieve relatively better accuracy, even if the error of the trajectory before fusion is large, which demonstrates that our method can still maintain a certain degree of accuracy in an environment where typical pose estimation methods have poor performance. In addition, the proposed time-calibration method also achieves high accuracy in estimating point correspondences.
KW  - mapping
KW  - continuous-time SLAM
KW  - deformation-based method
KW  - time calibration
Y1  - 2020
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-219988
SN  - 1424-8220
VL  - 20
IS  - 23
ER  -