Paper Info
Title
Curvefusion-A Method For Combining Estimated Trajectories With Applications To Slam And Time-Calibration
Abstract
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.
Year
DOI
Venue
2020
10.3390/s20236918
SENSORS
Keywords
DocType
Volume
mapping, continuous-time SLAM, deformation-based method, time calibration
Journal
20
Issue
ISSN
Citations 
23
1424-8220
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Shitong Du101.01
Helge A Lauterbach200.34
Xuyou Li301.01
Girum G Demisse400.34
Dorit Borrmann521617.74
Andreas Nüchter6134190.03