Fast detection of noisy GPS and magnetometer tags in wide-baseline multi-views

We propose an algorithm for detection of noisy GPS and magnetometer tags in wide-baseline camera views. Our algorithm neither needs densely sampled views nor does it need a single visually connected path through all the views in the dataset. We use vision-based estimates of mutual rotation and translation between cameras to compute a measure of confidence on the correctness of the associated GPS and magnetometer tags. The vision algorithm can find the epipolar geometry between two wide-baseline images without needing pre-specified correspondences. We have two versions of our approach; one that requires geometric pose estimation between all pairs of images and a faster version that uses a pre-filter based on photometric comparison of images to quickly reject non-overlapping views from further geometric consideration. We show qualitative and quantitative results on the Nokia Grand Challenge 2010 Dataset. We find that magnetometer readings are more accurate than GPS readings.