SPINS: A structure priors aided inertial navigation system

We propose a navigation system combining sensor-aided inertial navigation and prior-map-based localization to improve the stability and accuracy of robot localization in structure-rich environments. Specifically, we adopt point, line, and plane features in the navigation system to enhance the feature richness in low-texture environments and improve the localization reliability. We additionally integrate structure prior information of the environments to constrain the localization drifts and improve the accuracy. The prior information is called structure priors and parameterized as low-dimensional relative distances/angles between different geometric primitives. The localization is formulated as a graph-based optimization problem that contains sliding-window-based variables and factors, including Inertial Measurement Unit, heterogeneous features, and structure priors. A limited number of structure priors are selected based on the information gain to alleviate the computation burden. Finally, the proposed framework is extensively tested on synthetic data, public data sets, and, more importantly, on the real Unmanned Aerial Vehicle flight data obtained from both indoor and outdoor inspection tasks. The results show that the proposed scheme can effectively improve the accuracy and robustness of localization for autonomous robots in civilian applications.