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Lab member Saurav Agarwal successfully defends his Ph.D.!
Our heartiest congratulations to EDP Lab member Saurav Agarwal who successfully defended his Ph.D. on Dec 6, 2017. Saurav will be working full-time on a warehouse automation startup after his Ph.D.
Paper on RFM-SLAM is Accepted to ICRA 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!
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 […]
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 […]
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.
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 […]