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Paper on RFM-SLAM is Accepted to ICRA 2017

January 15, 2017

Our paper on a novel technique (RFM-SLAM) for 2D feature based SLAM has been accepted to ICRA 2017 to be held in Singapore!

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Can a robot drive 100s of Kilometers with < 1 m Error?

November 29, 2016

A considerably difficult aspect of Simultaneous Localization and Mapping (SLAM) is the problem of uncertainty constrained long term point-to-point navigation where global loop closures to eliminate estimation biases may not be possible. In such scenarios, a prime concern is to control the rate of localization error growth. We have developed fundamental results on the underlying […]

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EDPLab Develops a Novel Technique For Feature-Based SLAM

October 12, 2016

The SLAM problem is known to have a special property that when robot orientation is known, estimating the history of robot poses and feature locations can be posed as a standard linear least squares problem. We have developed a SLAM framework that uses relative feature-to-feature measurements to exploit this structural property of SLAM. Relative feature […]

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