Aggregation Functions for Simultaneous Attitude and Image Estimation With Event Cameras at High Angular Rates

Abstract

For fast-moving event cameras, projection of events onto the image frame exhibits smearing of events analogous to high motion blur. For camera attitude estimation, this presents a causality dilemma where motion prior is required to unsmear events, but an image prior is required to estimate motion. This dilemma is typically circumvented by including an IMU to provide motion priors. However, IMUs limited dynamic range of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\pm$</tex-math></inline-formula> <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$2000 \,\mathrm{^{\circ }}/\mathrm{s}$</tex-math></inline-formula> are shown to be insufficient for high angular rate rotorcrafts. Contrast Maximization is an event-only optimization framework that computes the optimal motion compensation parameter while generating an event image simultaneously. This letter analyses the performance of existing aggregation functions of the contrast maximization framework and proposes a non-convolution-based aggregation function that outperforms existing implementations. The use of discrete event images for optimizers is discussed, demonstrating alternate avenues of the framework to exploit. The effect of motion blur in motion-compensated images is defined and studied for Contrast Maximisation at high angular rates. Lastly, the framework is applied to rotation datasets with angular rates exceeding <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$2000 \,\mathrm{^{\circ }}/\mathrm{s}$</tex-math></inline-formula> to demonstrate high angular rate motion estimation without motion priors.