Probabilistic Plane Extraction and Modeling for Active Visual-Inertial Mapping

This paper presents an active visual-inertial mapping framework with points and planes. The key aspect of the proposed framework is a novel probabilistic plane extraction with its associated model for estimation. The approach allows the extraction of plane parameters and their uncertainties based on a modified version of PlaneRCNN [1]. The extracted probabilistic plane features are fused with point features in order to increase the robustness of the estimation system in texture-less environments, where algorithms based on points alone would struggle. A visual-inertial framework based on Iterative Extended Kalman filter (IEKF) is used to demonstrate the approach. The IEKF equations are customized through a measurement extrapolation method, which enables the estimation to handle the delay introduced by the neural network inference time systematically. The system is encompassed within an active mapping framework, based on Informative Path Planning to find the most informative path for minimizing map uncertainty in visual-inertial systems. The results from the conducted experiments with a stereo/IMU system mounted on a robotic arm show that introducing planar features to the map, in order to complement the point features in the state estimation, improves robustness in texture-less environments.