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AEROS: AdaptivE RObust Least-Squares for Graph-Based SLAM.

Milad Ramezani1,2, Matias Mattamala2, Maurice Fallon2

  • 1Robotics and Autonomous Systems Group, DATA61, CSIRO, Brisbane, QLD, Australia.

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Summary
This summary is machine-generated.

This study introduces AEROS, a new method for robust Simultaneous Localisation and Mapping (SLAM). AEROS adaptively minimizes errors from loop-closure outliers, improving trajectory accuracy in real-world scenarios.

Keywords:
SLAMback-end optimisationfactor graphleast squares minimisationoutlier resilienceperceptionrobust cost function

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Area of Science:

  • Robotics
  • Computer Vision
  • Artificial Intelligence

Background:

  • Outliers in loop-closure measurements significantly degrade Simultaneous Localisation and Mapping (SLAM) performance.
  • Existing methods often use hard switches for loop-closure constraints, which can be suboptimal.

Purpose of the Study:

  • To develop a novel approach, AEROS, for adaptive robust least-squares minimization in SLAM.
  • To improve the accuracy and reliability of robot trajectory estimation by effectively handling outliers.

Main Methods:

  • AEROS introduces a single latent parameter for adaptive robust cost minimization.
  • It performs joint optimization of sensor poses and the latent parameter.
  • The problem is formulated using Gaussian factors for compatibility with incremental estimation methods like iSAM.

Main Results:

  • AEROS demonstrates superior curve fitting to residual distributions, effectively reducing outlier impact.
  • Experimental results on synthetic and real-world LiDAR-SLAM datasets show competitiveness with state-of-the-art techniques.
  • The approach proves particularly effective in real-world applications.

Conclusions:

  • AEROS offers a generalized and adaptive solution for robust cost minimization in the SLAM back-end.
  • It enhances trajectory estimation accuracy and robustness against loop-closure outliers.
  • The method is compatible with existing incremental SLAM solvers.