Efficient Online Egomotion Estimation using Visual and Inertial Readings

An egomotion estimator that makes use of the com-plementary strengths of inertial and visual readings is introduced in the context of efficient and robust real-time motion estimation. Our work is targeted to a noisy, computationally limited and unconstrained envi-ronment. Experimental results show that the proposed technique, despite being built upon heuristics that often privilege speed over exhaustiveness, produces extremely precise and robust egomotion estimates in situations that cripple visual-only and inertial-only trackers.