Visual odometry estimates the transformations between
consecutive frames of a video stream in order to recover the camera’s trajectory. As this approach does not
require to build a map of the observed environment, it
is fast and simple to implement. In the last decade RGBD cameras proliferated in roboTIcs, being also the sensors
of choice for many practical visual odometry systems. Although RGB-D cameras provide readily available depth
images, that greatly simplify the frame-to-frame transformations computaTIon, the number of numerical parameters that have to be set properly in a visual odometry
system to obtain an accurate trajectory estimate remains
high. Whereas seƫng them by hand is certainly possible,
it is a tedious try-and-error task. Therefore, in this article
we make an assessment of two population-based approaches to parameter opTImizaTIon, that are for long time
applied in various areas of robotics, as means to find best
parameters of a simple RGB-D visual odometry system.
The optimization algorithms investigated here are particle swarm optimization and an evolutionary algorithm
variant. We focus on the optimization methods themselves, rather than on the visual odometry algorithm, seeking an efficient procedure to find parameters that minimize the estimated trajectory errors. From the experimental results we draw conclusions as to both the efficiency of the optimization methods, and the role of particular parameters in the visual odometry system.
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