Volume IV-2/W4
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2/W4, 91-98, 2017
https://doi.org/10.5194/isprs-annals-IV-2-W4-91-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2/W4, 91-98, 2017
https://doi.org/10.5194/isprs-annals-IV-2-W4-91-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.

  13 Sep 2017

13 Sep 2017

EVALUATING CONTINUOUS-TIME SLAM USING A PREDEFINED TRAJECTORY PROVIDED BY A ROBOTIC ARM

B. Koch, R. Leblebici, A. Martell, S. Jörissen, K. Schilling, and A. Nüchter B. Koch et al.
  • Informatics VII – Robotics and Telematics, Julius-Maximilians University Würzburg, Germany

Keywords: 3D laser scanning, SLAM, continuous trajectory optimization, ground truth, evaluation

Abstract. Recently published approaches to SLAM algorithms process laser sensor measurements and output a map as a point cloud of the environment. Often the actual precision of the map remains unclear, since SLAMalgorithms apply local improvements to the resulting map. Unfortunately, it is not trivial to compare the performance of SLAMalgorithms objectively, especially without an accurate ground truth. This paper presents a novel benchmarking technique that allows to compare a precise map generated with an accurate ground truth trajectory to a map with a manipulated trajectory which was distorted by different forms of noise. The accurate ground truth is acquired by mounting a laser scanner on an industrial robotic arm. The robotic arm is moved on a predefined path while the position and orientation of the end-effector tool are monitored. During this process the 2D profile measurements of the laser scanner are recorded in six degrees of freedom and afterwards used to generate a precise point cloud of the test environment. For benchmarking, an offline continuous-time SLAM algorithm is subsequently applied to remove the inserted distortions. Finally, it is shown that the manipulated point cloud is reversible to its previous state and is slightly improved compared to the original version, since small errors that came into account by imprecise assumptions, sensor noise and calibration errors are removed as well.