Evaluating continuous-time SLAM using a predefined trajectory provided by a robotic arm

Evaluating continuous-time SLAM using a predefined trajectory provided by a robotic arm. Koch, B., Leblebici, R., Martell, A., Jörissen, S., Schilling, K., & Nüchter, A. In Proceedings of the ISPRS Geospatial Week 2017, Laserscanning 2017, of ISPRS Annals Photogrammetry and Remote Sensing, Spatial Inf. Sci., IV-2/W4, pages 17–23, Wuhan, China, September, 2017.

Paper doi abstract bibtex

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.

@inproceedings{LS2017,
  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.},
  added-at = {2017-11-08T15:05:44.000+0100},
  address = {Wuhan, China},
  author = {Koch, B. and Leblebici, R. and Martell, A. and J{\"o}rissen, S. and Schilling, K. and N{\"u}chter, A.},
  biburl = {https://www.bibsonomy.org/bibtex/21ef33e57269c2972850d61942dc28f3e/nuechter76},
  booktitle = {Proceedings of the ISPRS Geospatial Week 2017, Laserscanning 2017},
  doi = {10.5194/isprs-annals-IV-2-W4-91-2017},
  interhash = {127fd98610ca2eb215fc027b106f0b22},
  intrahash = {1ef33e57269c2972850d61942dc28f3e},
  keywords = {imported},
  month = {September},
  pages = {17--23},
  series = {ISPRS Annals Photogrammetry and Remote Sensing, Spatial Inf. Sci., IV-2/W4},
  timestamp = {2018-03-07T21:58:35.000+0100},
  title = {Evaluating continuous-time SLAM using a predefined
                  trajectory provided by a robotic arm},
  url = {https://robotik.informatik.uni-wuerzburg.de/telematics/download/ls2017.pdf},
  year = 2017
}

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