**5. Concluding Remarks**

This paper proposes a loosely integrated feature tracking method on event frames using event, intensity, and inertial information to improve the accuracy and efficiency problem. MHT-BP, which involves four-parameter affine transformation and batch processing, is proposed to achieve fast and short-term feature matching. Then, a weighted fusion algorithm involving the constant velocity model and the stochastic model for drifts is proposed to reduce drifts. Next, it corrects the drift by weighted fusion in the way of post-processing, which is still meaningful for event-camera-based applications, such as SfM and SLAM.

MHT-BP is compared with FasT-Match, showing better efficiency at the expense of slight accuracy decline. In comparison with three state-of-the-art methods, including both event-information-based methods (PDAT and HTRT) and one multiple-sensor fusion-based method (EKLT), WF-MHT-BP shows the significant superiority on accuracy and efficiency and comparable feature-tracking lengths with EKLT.

In the future, the work can be refined and extended in the following aspects: First, feature detection is still conducted on intensity frames in the initialization and re-detection stage. If more features are needed, but intensity images have not arrived, the accuracy of high-level tasks, such as VO and SLAM, may be affected. Therefore, feature detection on event frames still needs to be further explored. Second, the final goal of event-frame-based feature tracking is to improve the robustness in challenging environments or motions. Thus, future work will focus on the application of WF-MHT-BP on high-level tasks, such as event-camera-based localization and mapping applications.

**Author Contributions:** Conceptualization, X.L. and Z.L.; methodology, Z.L.; software and validation, Z.L.; writing—original draft preparation, Z.L., F.Z., and Y.L.; writing—review and editing, F.Z. and Y.L.; supervision, Y.L.; project administration, Y.L.; funding acquisition Z.L. and Y.L. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by project ZR2021QD148 supported by Shandong Provincial Natural Science Foundation and the Open Research Project (grant number ICT2021B17) of the State Key Laboratory of Industrial Control Technology, Zhejiang University, China.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** The data are available at URL: https://rpg.ifi.uzh.ch/davis\_data.html (accessed on 29 January 2022).

**Conflicts of Interest:** The authors declare no conflict of interest.
