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Paper WeA07.3

Furgale, Paul Timothy (Eidgenössische Technische Hochschule Zürich), Barfoot, Timothy (University of Toronto), Sibley, Gabe (George Washington University)

Continuous-Time Batch Estimation Using Temporal Basis Functions

Scheduled for presentation during the Regular Session "SLAM I" (WeA07), Wednesday, May 16, 2012, 09:00−09:15, Meeting Room 7 (Remnicha)

2012 IEEE International Conference on Robotics and Automation, May 14-18, 2012, RiverCentre, Saint Paul, Minnesota, USA

This information is tentative and subject to change. Compiled on June 19, 2018

Keywords SLAM, Sensor Fusion, Calibration and Identification

Abstract

Roboticists often formulate estimation problems in discrete time for the practical reason of keeping the state size tractable. However, the discrete-time approach does not scale well for use with high-rate sensors, such as inertial measurement units or sweeping laser imaging sensors. The difficulty lies in the fact that a pose variable is typically included for every time at which a measurement is acquired, rendering the dimension of the state impractically large for large numbers of measurements. This issue is exacerbated for the simultaneous localization and mapping (SLAM) problem, which further augments the state to include landmark variables. To address this tractability issue, we propose to move the full maximum likelihood estimation (MLE) problem into continuous time and use temporal basis functions to keep the state size manageable. We present a full probabilistic derivation of the continuous-time estimation problem, derive an estimator based on the assumption that the densities and processes involved are Gaussian, and show how coefficients of a relatively small number of basis functions can form the state to be estimated, making the solution efficient. Our derivation is presented in steps of increasingly specific assumptions, opening the door to the development of other novel continuous-time estimation algorithms using different assumptions. Results from a self-calibration experiment involving a camera and a high-rate IMU are provided to validate the approach.

 

 

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