Visual time to collision estimation for small or micro air vehicles is challenging due to aggressive 6-DOF motion, real time performance requirements and significant size, weight and power constraints of the platform. Recent work in collision detection using insect inspired optical flow based methods have been demonstrated in low power hardware implementations [1][2][3][4], but have not achieved the obstacle detection and false alarm rate performance necessary for practical deployment. This performance is sensitive to correspondence errors in the optical flow field, so one approach to improving performance is to use a richer feature set for correspondence, along with calibrated inertial information from the platform to aid correspondence. In this video, we show proof of concept results for such an approach. Estimation results are noisy, but encouraging, and given that SIFT feature correspondence has been demonstrated in real time on low power GPUs, it has the potential for future small UAV integration.