Uncertainty Model for Template Feature Matching

Using visual odometry and inertial measurements, indoor and outdoor positioning systems can perform an accurate self-localization in unknown, unstructured environments where absolute positioning systems (e.g. GNSS) are unavailable. However, the achievable accuracy is highly affected by the residuals of calibration, the quality of the noise model, etc. Only if these unavoidable uncertainties of sensors and data processing can be taken into account and be handled via error propagation, which allows to propagate them through the entire system. The central filter (e.g. Kalman filter) of the system can then make use of the enhanced statistical model and use the propagated errors to calculate the optimal result. In this paper, we focus on the uncertaintiy calculation of the elementary part of the optical navigation, the template feature matcher. First of all, we propose a method to model the image noise. Then we use Taylor’s theorem to extend two very popular and efficient template feature matchers sum-of-absolute-differences (SAD) and normalized-cross-correlation (NCC) to get sub-pixel matching results. Based on the proposed noise model and the extended matcher, we propagate the image noise to the uncertainties of sub-pixel matching results. Although the SAD and NCC are used, the image noise model can be easily combined with other feature matchers. We evaluate our method by an Integrated Positioning System (IPS) which is developed by German Aerospace Center. The experimental results show that our method can improve the quality of the measured trajectory. Moreover, it increases the robustness of the system.