Visual-inertial curve simultaneous localization and mapping: Creating a sparse structured world without feature points

Abstract

We present a simultaneous localization and mapping (SLAM) algorithm that uses Bézier curves as static landmark primitives rather than feature points. Our approach allows us to estimate the full six degrees of freedom pose of a robot while providing a structured map that can be used to assist a robot in motion planning and control. We demonstrate how to reconstruct the three-dimensional (3D) location of curve landmarks from a stereo pair and how to compare the 3D shape of curve landmarks between chronologically sequential stereo frames to solve the data association problem. We also present a method to combine curve landmarks for mapping purposes, resulting in a map with a continuous set of curves that contain fewer landmark states than conventional point-based SLAM algorithms. We demonstrate our algorithm's effectiveness with numerous experiments, including comparisons to existing state-of-the-art SLAM algorithms.

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