A humanoid robot is expected to perform tasks in human-engineered environments by having the ability to operate tools typically used by humans and navigate by walking, climbing stairs and opening doors. Considering this scenario, the problem addressed in this work is mapping and localisation for a humanoid robot within a room-sized environment.
The robot is equipped with proprioceptive sensors providing kinematic and inertial information as well as exteroceptive sensors such as a stereo camera pair. Challenges to a typical visual mapping method for this application include motion blur introduced in the camera image as the robot is walking, self-detection, dynamic elements in the environment, as well as a lack of visual features. The robot performs locally loopy trajectories and repeatedly observes the same objects, requiring for a method which supports multiple re-entry. In order to overcome some of these challenges, the approach is to fuse proprioceptive data from kinematics and inertial sensors within a dense visual SLAM method in order to produce more accurate position estimates and higher quality maps.
Master's Thesis Title: Coordinated Solutions for multiple vehicle navigation and data exchange in the maritime domain
Underwater navigation is difficult. EM waves do not propagate well and therefore GPS is not available. It is also a largely unstructured and feature-poor environment where terrain based solutions are challenging to apply. The problem is traditionally tackled using a combination of expensive inertial sensors and acoustic based positioning systems requiring careful deployment and calibration and limiting the operational area of the system. The project will research novel techniques in navigation using a mixture of assets (surface vehicle with GPS, underwater vehicles with INS and acoustics) to optimise their localisation whilst maximising the communication bandwidth available between the various assets. The system will be developed in simulation first, using our in-house simulation tools and then ported onto the vehicles acquired as part of robotarium to validate the system in real conditions.
Research interests: Probabilistic robotics, state-estimation, SLAM