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Invariant EKF Design for Scan Matching-aided Localization

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arxiv 1503.01407 v1 pith:ZAWBJTNZ submitted 2015-03-04 cs.SY cs.ROcs.SY

Martin Barczyk , Silvère Bonnabel , Jean-Emmanuel Deschaud , François Goulette This is my paper · ORCID
classification cs.SYcs.RO
keywords designextendedfilteriekfinvariantkalmanlocalizationscan
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Localization in indoor environments is a technique which estimates the robot's pose by fusing data from onboard motion sensors with readings of the environment, in our case obtained by scan matching point clouds captured by a low-cost Kinect depth camera. We develop both an Invariant Extended Kalman Filter (IEKF)-based and a Multiplicative Extended Kalman Filter (MEKF)-based solution to this problem. The two designs are successfully validated in experiments and demonstrate the advantage of the IEKF design.

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Cited by 1 Pith paper

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  1. Constrained Stein Variational Gradient Descent for Robot Perception, Planning, and Identification

    cs.RO 2025-05 conditional novelty 5.0 of 10

    The authors introduce constrained SVGD frameworks and demonstrate collision-free planning, constrained inverse kinematics, and pose estimation with table placement constraints.

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