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This work proposes a new, online algorithm for estimating the local scalecorrection to apply to the output of a monocular SLAM system and obtain an asfaithful as possible metric reconstruction of the 3D map and of the cameratrajectory. Within a Bayesian framework, it integrates observations from adeep-learning based generic object detector and a prior on the evolution of thescale drift. For each observation class, a predefined prior on the heights ofthe class objects is used. This allows to define the observations likelihood.Due to the scale drift inherent to monocular SLAM systems, we integrate a roughmodel on the dynamics of scale drift. Quantitative evaluations of the systemare presented on the KITTI dataset, and compared with different approaches. Theresults show a superior performance of our proposal in terms of relativetranslational error when compared to other monocular systems.

Bayesian Scale Estimation for Monocular SLAM Based on Generic Object Detection for Correcting Scale Drift