Single and Cooperative Imaging based GPS Denied Localisation Solution for Autonomous Platforms.

To employ the use of Visual Odometry (VO) and the Passive Thermal Imagery (TI) in order to tackle the fundamental problem of reliable and efficient camera based multi-target tracking systems. The aim is to produce a significantly more convenient solution that is capable of tracking and classifying targets between the ranges of sensors in the network. The main interference to the solution would arise from cloud and other obscurants, other problem would include varying illumination levels; this would be particularly significant should platforms wish to remain stealthy.
Visual Odometry has been used in wide range of robotic applications that estimate the motion of an object from images taken at regular intervals. Due to advances in Plenoptic technology, it is likely that unison of visual Odometry and Plenoptic technology would outperform similar algorithms such as Simultaneous Localisation and Mapping (SLAM). As VO focuses on relative position(s) rather than mapping the terrain, there exists a possibility to enhance a baseline solution by the use of depth mapping. This therefore provides the possibility to create a sensor base network which can identify multiple targets and track their propagation throughout the network providing the backbone to an effective real time multi-target tracking solution.
The challenge of varying illumination levels can be addressed by the use of passive thermal imagery - due to the fact TI detects an objects emittance rather than its reflection and so can be used at night. The use of TI is problematic as it would make the solution prone to low spatial resolution, history effects, variations in temperature, and low signal-to-noise ratios. This is an exciting challenge an excellent opportunity to develop new image processing methods for these particular types of sensors.
The use of Deep Convolutional Neural Networks and Deep Q Learning has the potential to tackle the problem of reliable and efficient target recognition. In recent years deep learning has advanced to the point that real time object detection and classification have become feasible - even on relatively lacklustre machines. This means the combination of Deep Convolutional Neural Networks and LSTM techniques employed on the sensor network provides a possible solution to a multi-target tracking system.