RRVPE: A Robust and Real-Time Visual-Inertial-GNSS Pose Estimator for Aerial Robot Navigation

Abstract

Self-localization and orientation estimation are the essential capabilities for mobile robot navigation. In this article, a robust and real-time visual-inertial-GNSS(Global Navigation Satellite System) tightly coupled pose estimation (RRVPE) method for aerial robot navigation is presented. The aerial robot carries a front-facing stereo camera for self-localization and an RGB-D camera to generate 3D voxel map. Ulteriorly, a GNSS receiver is used to continuously provide pseudorange, Doppler frequency shift and universal time coordinated (UTC) pulse signals to the pose estimator. The proposed system leverages the Kanade Lucas algorithm to track Shi-Tomasi features in each video frame, and the local factor graph solution process is bounded in a circumscribed container, which can immensely abandon the computational complexity in nonlinear optimization procedure. The proposed robot pose estimator can achieve camera-rate (30 Hz) performance on the aerial robot companion computer. We thoroughly experimented the RRVPE system in both simulated and practical circumstances, and the results demonstrate dramatic advantages over the state-of-the-art robot pose estimators.

Perceiving like a Bat: Hierarchical 3D Geometric–Semantic Scene Understanding Inspired by a Biomimetic Mechanism

“…Finally, we utilized our self-developed drone to perform a 3D geometric–semantic scene-understanding test in the real world (in Section 6.3 ). The evaluation for robot state estimation has already been performed in our previous work [ 32 , 33 , 34 , 35 ], so is not a contribution of this article.…”

“…The evaluation for robot state estimation has already been performed in our previous work [32][33][34][35], so is not a contribution of this article. Cityscapes [42] is a large-scale dataset that focuses on scene understanding in an urban street background.…”

“…Finally, the UAV pose can be obtained by optimizing the UAV state vector by employing probability factor graph optimization. For the detailed process of factor graph optimization, please refer to our previous literature [ 32 , 33 , 34 , 35 ].…”

“…Finally, the UAV pose can be obtained by optimizing the UAV state vector  by employing probability factor graph optimization. For the detailed process of factor graph optimization, please refer to our previous literature [32][33][34][35]. Assuming that the measurement noise conforms to a Gaussian distribution with zero mean, then the solution process of the UAV state vector χ can be expressed as:…”