TensorMap: Lidar-based topological mapping and localization VIA tensor decompositions

Sirisha Rambhatla, Nikos D. Sidiropoulos, Jarvis Haupt

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We propose a technique to develop (and localize in) topological maps from light detection and ranging (Lidar) data. Localizing an au-tonomous vehicle with respect to a reference map in real-time is crucial for its safe operation. Owing to the rich information provided by Lidar sensors, these are emerging as a promising choice for this task. However, since a Lidar outputs a large amount of data every fraction of a second, it is progressively harder to process the information in real-time. Consequently, current systems have migrated towards faster alternatives at the expense of accuracy. To overcome this inherent trade-off between latency and accuracy, we propose a technique to develop topological maps from Lidar data using the orthogonal Tucker3 tensor decomposition. Our experimental evaluations demonstrate that in addition to achieving a high compression ratio as compared to full data, the proposed technique, TensorMap, also accurately detects the position of the vehicle in a graph-based representation of a map. We also analyze the robustness of the pro-posed technique to Gaussian and translational noise, thus initiating explorations into potential applications of tensor decompositions in Lidar data analysis.

Original languageEnglish (US)
Title of host publication2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1368-1372
Number of pages5
ISBN (Electronic)9781728112954
DOIs
StatePublished - Feb 20 2019
Event2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Anaheim, United States
Duration: Nov 26 2018Nov 29 2018

Publication series

Name2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings

Conference

Conference2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018
CountryUnited States
CityAnaheim
Period11/26/1811/29/18

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Keywords

  • Lidar
  • Localization of autonomous vehicles
  • Orthogonal Tucker decompositions
  • Scan-matching
  • Topological maps

Cite this

Rambhatla, S., Sidiropoulos, N. D., & Haupt, J. (2019). TensorMap: Lidar-based topological mapping and localization VIA tensor decompositions. In 2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings (pp. 1368-1372). [8646665] (2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/GlobalSIP.2018.8646665