'Institute of Electrical and Electronics Engineers (IEEE)'
Abstract
Point-based stereo visual odometry systems
typically estimate the camera motion by minimizing a cost function of the projection residuals between consecutive frames. Under some mild assumptions, such minimization is equivalent to maximizing
the probability of the measured residuals given
a certain pose change, for which a suitable model of the error distribution (sensor model) becomes of capital importance in order to obtain accurate results. This paper proposes a robust probabilistic model for projection errors, based on real world data. For that,
we argue that projection distances follow Gamma
distributions, and hence, the introduction of these
models in a probabilistic formulation of the motion
estimation process increases both precision and accuracy. Our approach has been validated through a series of experiments with both synthetic and real data, revealing an improvement in accuracy while not increasing the computational burden.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. Project "PROMOVE: Advances in mobile robotics for promoting independent life of elders", funded by the Spanish Government and the "European Regional
Development Fund ERDF" under contract DPI2014-55826-R