Range-only SLAM with Interpolated Range Data

In a series of recent papers Singh et al. have explored the idea of Simultaneous Localization and Mapping (SLAM) using range-only measurements. These measurements, obtained from radio or sonar sensors, come at irregular time intervals. In this report we explore the use of interpolation to generate data equally spaced in time, in order to improve the performance of SLAM algorithms. We test this idea on several (simulated and real) robot paths and two SLAM algorithms: an online Extended Kalman Filter (EKF) algorithm and an offline batch optimization algorithm.

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