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

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

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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Decentralized State Estimation via a Hybrid of Consensus and Covariance intersection – Technical Report

This paper presents a new recursive information consensus filter for decentralized dynamic-state estimation. No structure is assumed about the topology of the network and local estimators are assumed to have access only to local information. The network need not be connected at all times. Consensus over priors which might become correlated is performed through Covariance Intersection (CI) and consensus over new […]

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Lab member Saurav Agarwal will be at Qualcomm Research

Lab member Saurav Agarwal will be interning at Qualcomm Research in San Diego for the summer and fall of 2015 where he will work on autonomous vision-based navigation for micro aerial vehicles.

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Feedback-based Information RoadMaps with Open Motion Planning Library

We are pleased to announce that the developers of OMPL (Open Motion Planning Library) at Rice University have posted a guest article by our lab on our latest work on integrating FIRM with OMPL. We would particularly like to acknowledge the support of Mark Moll in this process. Feel free to browse our code on […]

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Bringing Belief Space Planning to Physical Systems

Sampling based deterministic motion planning has shown great success in the past. However, as we progress towards more realistic modeling and planning for robotic systems, we need to account for uncertainties in our systems. Uncertainties mainly arise from: 1. Sensing or measurement noise (also called observation noise) i.e. sensors do not give perfect measurements, instead […]

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