Optimal motion estimation from visual and inertial measurements

TLDR: An algorithm is presented that computes optimal vehicle motion estimates by considering all of the measurements from a camera, rate gyro, and accelerometer simultaneously, and shows that using image and inertial data together can produce highly accurate estimates even when the results produced by each modality alone are very poor.

Abstract: Cameras and inertial sensors are good candidates to be deployed together for autonomous vehicle motion estimation, since each can be used to resolve the ambiguities in the estimated motion that results from using the other modality alone. We present an algorithm that computes optimal vehicle motion estimates by considering all of the measurements from a camera, rate gyro, and accelerometer simultaneously. Such optimal estimates are useful in their own right, and as a gold standard for the comparison of online algorithms. By comparing the motions estimated using visual and inertial measurements, visual measurements only, and inertial measurements only against ground truth, we show that using image and inertial data together can produce highly accurate estimates even when the results produced by each modality alone are very poor Our test datasets include both conventional and omnidirectional image sequences, and an image sequence with a high percentage of missing data.