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Visual odometry algorithms tend to degrade when facing low-textured scenes-from e.g. human-made environments-, where it is often difficult to find asufficient number of point features. Alternative geometrical visual cues, suchas lines, which can often be found within these scenarios, can becomeparticularly useful. Moreover, these scenarios typically present structuralregularities, such as parallelism or orthogonality, and hold the ManhattanWorld assumption. Under these premises, in this work, we introduce MSC-VO, anRGB-D -based visual odometry approach that combines both point and linefeatures and leverages, if exist, those structural regularities and theManhattan axes of the scene. Within our approach, these structural constraintsare initially used to estimate accurately the 3D position of the extractedlines. These constraints are also combined next with the estimated Manhattanaxes and the reprojection errors of points and lines to refine the camera poseby means of local map optimization. Such a combination enables our approach tooperate even in the absence of the aforementioned constraints, allowing themethod to work for a wider variety of scenarios. Furthermore, we propose anovel multi-view Manhattan axes estimation procedure that mainly relies on linefeatures. MSC-VO is assessed using several public datasets, outperforming otherstate-of-the-art solutions, and comparing favourably even with some SLAMmethods.

MSC-VO: Exploiting Manhattan and Structural Constraints for Visual Odometry