An estimation framework is presented that improves the robustness of GPS-denied state estimation to changing environmental conditions by fusing updates from multiple view-based odometry algorithms. This allows the vehicle to utilize a suite of complementary exteroceptive sensors or sensing modalities. By estimating the vehicle states relative to a local coordinate frame collocated with an odometry keyframe, observability of the relative state is maintained. A description of the general framework is given, as well as the specific equations for a multiplicative extended Kalman filter with a multirotor vehicle. Experimental results are presented that demonstrate the ability of the proposed algorithm to produce accurate and consistent estimates in challenging environments that cause a single-sensor solution to fail.
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