Title: Bayesian Scale Estimation for Monocular SLAM Based on Generic Object Detection for Correcting Scale Drift,
Abstract: This work proposes a new, online algorithm for estimating the local scale
correction to apply to the output of a monocular SLAM system and obtain an as
faithful as possible metric reconstruction of the 3D map and of the camera
trajectory. Within a Bayesian framework, it integrates observations from a
deep-learning based generic object detector and a prior on the evolution of the
scale drift. For each observation class, a predefined prior on the heights of
the class objects is used. This allows to define the observations likelihood.
Due to the scale drift inherent to monocular SLAM systems, we integrate a rough
model on the dynamics of scale drift. Quantitative evaluations of the system
are presented on the KITTI dataset, and compared with different approaches. The
results show a superior performance of our proposal in terms of relative
translational error when compared to other monocular systems.