Real Time SLAM Using Compressed Occupancy Grids For a Low Cost Autonomous Underwater Vehicle
Abstract
The research presented in this dissertation pertains to the development of a real time
SLAM solution that can be performed by a low cost autonomous underwater vehicle equipped
with low cost and memory constrained computing resources. The design of a custom
rangefinder for underwater applications is presented. The rangefinder makes use of two laser
line generators and a camera to measure the unknown distance to objects in an underwater
environment. A visual odometry algorithm is introduced that makes use of a downward
facing camera to provide our underwater vehicle with localization information. The sensor
suite composed of the laser rangefinder, downward facing camera, and a digital compass
are verified, using the Extended Kalman Filter based solution to the SLAM problem along
with the particle filter based solution known as FastSLAM, to ensure that they provide in-
formation that is accurate enough to solve the SLAM problem for out low cost underwater
vehicle. Next, an extension of the FastSLAM algorithm is presented that stores the map of
the environment using an occupancy grid is introduced. The use of occupancy grids greatly
increases the amount of memory required to perform the algorithm so a version of the Fast-
SLAM algorithm that stores the occupancy grids using the Haar wavelet representation is
presented. Finally, a form of the FastSLAM algorithm is presented that stores the occupancy
grid in compressed form to reduce the amount memory required to perform the algorithm.
It is shown in experimental results that the same result can be achieved, as that produced
by the algorithm that stores the complete occupancy grid, using only 40% of the memory
required to store the complete occupancy grid.
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