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Map-Based Probabilistic Visual Self-Localization





Accurate and efficient self-localization is a critical problem for autonomous systems. This paper describes an affordable solution to vehicle self-localization which uses odometry computed from two video cameras and road maps as the sole inputs. The core of the method is a probabilistic model for which an efficient approximate inference algorithm is derived. The inference algorithm is able to utilize distributed computation in order to meet the real-time requirements of autonomous systems in some instances. Because of the probabilistic nature of the model the method is capable of coping with various sources of uncertainty including noise in the visual odometry and inherent ambiguities in the map (e.g., in a Manhattan world). By exploiting freely available, community developed maps and visual odometry measurements, the proposed method is able to localize a vehicle to 4m on average after 52 seconds of driving on maps which contain more than 2,150km of drivable roads.

Author(s): Marcus A. Brubaker and Andreas Geiger and Raquel Urtasun
Journal: IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI)
Year: 2016

Department(s): Autonomous Vision, Perceiving Systems
Research Project(s): Global Localization and Affordance Learning
Bibtex Type: Article (article)

Links: pdf


  title = {Map-Based Probabilistic Visual Self-Localization},
  author = {Brubaker, Marcus A. and Geiger, Andreas and Urtasun, Raquel},
  journal = {IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI)},
  year = {2016}