Laser-Radar Data Fusion with Gaussian Process Implicit Surfaces

  • Marcos P. Gerardo-Castro
  • Thierry Peynot
  • Fabio Ramos
Part of the Springer Tracts in Advanced Robotics book series (STAR, volume 105)


This work considers the problem of building high-fidelity 3D representations of the environment from sensor data acquired by mobile robots. Multi-sensor data fusion allows for more complete and accurate representations, and for more reliable perception, especially when different sensing modalities are used. In this paper, we propose a thorough experimental analysis of the performance of 3D surface reconstruction from laser and mm-wave radar data using Gaussian Process Implicit Surfaces (GPIS), in a realistic field robotics scenario. We first analyse the performance of GPIS using raw laser data alone and raw radar data alone, respectively, with different choices of covariance matrices and different resolutions of the input data. We then evaluate and compare the performance of two different GPIS fusion approaches. The first, state-of-the-art approach directly fuses raw data from laser and radar. The alternative approach proposed in this paper first computes an initial estimate of the surface from each single source of data, and then fuses these two estimates. We show that this method outperforms the state of the art, especially in situations where the sensors react differently to the targets they perceive.


Point Cloud Mobile Robot Data Fusion Fusion Method Radar Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Marcos P. Gerardo-Castro
    • 1
  • Thierry Peynot
    • 1
  • Fabio Ramos
    • 2
  1. 1.Australian Centre for Field Robotics (ACFR)The University of SydneySydneyAustralia
  2. 2.ACFR, School of Information TechnologiesThe University of SydneySydneyAustralia

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