Particle Filter Localization on Continuous Occupancy Maps

  • Alberto Yukinobu HataEmail author
  • Denis Fernando Wolf
  • Fabio Tozeto Ramos
Conference paper
Part of the Springer Proceedings in Advanced Robotics book series (SPAR, volume 1)


Occupancy grid maps have been widely used for robot localization. Despite the popularity, this representation has some limitations, such as requirement of discretization of the environment, assumption of independence between grid cells and necessity of dense sensor data. Suppressing these limitations can improve the localization performance, but requires a different representation of the environment. Gaussian process occupancy map (GPOM) is a novel representation based on Gaussian Process that enables the construction of continuous maps (i.e. without discretization) using few laser measurements. This paper addresses a new localization method that uses GPOM to estimate the robot pose in areas not directly observed during mapping and generally provides higher accuracy compared to occupancy grid maps localization. Specifically, we devised a novel likelihood model based on the multivariate normal probability density function and adapted the particle filter localization method to work with GPOM. Experiments showed localization errors more than three times lower in comparison with particle filter localization using occupancy grid maps.


Gaussian Process Occupancy Maps Gaussian Process Particle Filter Localization Occupancy Grid Map Sparse laser sensor data 



The authors acknowledge the grant provided by FAPESP (2012/02354-1; 2014/09096-3), the ACFR and LRM groups for their support.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Alberto Yukinobu Hata
    • 1
    Email author
  • Denis Fernando Wolf
    • 1
  • Fabio Tozeto Ramos
    • 2
  1. 1.Mobile Robotics LaboratoryUniversity of São PauloSão PauloBrazil
  2. 2.School of Information TechnologiesUniversity of SydneySydneyAustralia

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