A deep learning approach to state estimation from videos

Kalman lters have been commonly used for estimating the state of a vehicle from a video. Multi-State Constraint Kalman Filter (MSCKF) is an EKF-based state estimator that uses feature measurements for pose estimation of a vehicle. These models require a lot of hands-on engineering time to dene the measurement functions. We propose a data-driven approach by training deep neural networks on high-dimensional navigation image data generated from a simulation. We describe a CNN model that robustly learns reliable features from the input and gives promising results to model temporal data. We show that a deep learning approach can be a replacement for the MSCKF model for estimating the velocity of a moving vehicle.

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2018

Author dc:creator

• Doshi, Chandani

Advisor dc:contributor.advisor

• Rebecca L. Russell and Leslie P. Kaelbling.

• Electrical Engineering and Computer Science.

Statement dc:rights

• MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/119761

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/119761