Challenges in Computer Vision Intuitively appealing f computationally realizable Stable feature extraction is hard; results rarely general Extracted features are sparse Matching requires exponential time Matches are often wrong
Challenges in Computer Vision • Intuitively appealing �= computationally realizable • Stable feature extraction is hard; results rarely general • Extracted features are sparse • Matching requires exponential time • Matches are often wrong 6
Implications for Visual SLAM Hard to reliably find landmarks Really Hard to reliably find landmarks Really really hard to reliably find landmarks e Data association is slow and unreliable e False matches introduce substantial errors Accurate probabilistic models unavailable
Implications for Visual SLAM • Hard to reliably find landmarks • Really Hard to reliably find landmarks • Really Really Hard to reliably find landmarks • Data association is slow and unreliable • False matches introduce substantial errors • Accurate probabilistic models unavailable 7
Remarks on SIFT approach e For visual slam. landmarks must be identifiable across arge changes in distance Small changes in view direction (Bonus) Changes in illumination ● Solution: Produce"scale-invariant" image representation Extract points with associated scale information Use matcher empirically capable of handling small displacements
Remarks on SIFT approach • For visual SLAM, landmarks must be identifiable across: – Large changes in distance – Small changes in view direction – (Bonus) Changes in illumination • Solution: – Produce “scale-invariant” image representation – Extract points with associated scale information – Use matcher empirically capable of handling small displacements 8