PA-EVIO: Polarity-Aided Event–Visual–Inertial Odometry With Adaptive Event Representation

Event cameras have received extensive research interest because of their advantages over conventional cameras in high-speed motion and high-dynamic-range (HDR) environments. In this paper, we present a polarity-aided event-visual-inertial odometry system (PA-EVIO) that integrates the time-surface generation module, the feature processing module, and the state estimator module. Unlike the exponential decay with a fixed decay rate, the event adaptive decay, based on an event activity model, adjusts the decay rate according to the dynamics of the event stream and provides better texture performance. We propose a compensation method to enhance the robustness of feature tracking with polarity-weighted time surfaces and alleviate the adverse effect of polarity fluctuations caused by motion direction changes. The proposed odometry leverages the advantages of event cameras while also incorporating visual information to enhance robustness, particularly in scenarios where the event data is sparse or the image data is of good quality. We conduct extensive experiments to evaluate the proposed method, covering scenarios with high dynamic range and aggressive motion speed. The results from the DAVIS240C, HKU-Arclab, and UZH-FPV datasets indicate that our method outperforms other EIO and EVIO methods with real-time performance. The code will be released at https://github.com/APRIL-ZJU/PA-EVIO.