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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 […]
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 […]