An Improved Visual-Inertial Odometry Based on Self-Adaptive Attention-Anticipation Feature Selector

• 1、State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications

Abstract：Visual inertia odometers have achieved great success with the development of robot vision. However, it remains a challenging problem to achieve robust and efficient pose estimation on low-power platforms such as smartphones. This paper proposes a new visual inertial odometer scheme for low-power platforms, named visual inertial odometer based on adaptive attention-anticipation mechanism, which adds visual information to the VINS-based visual inertial odometer. The attention distribution module and the motion information forward anticipation module are controlled by the adaptive adjustment module to reduce the system operation load and improve the system tracking accuracy. We contribute in the following three aspects: 1) A attention mechanism for visual inertia history is proposed, which provides visual attention distribution for system radical motion in complex space environment, and extracts vision with high weight on system influence. Feature tracking; 2) A visual feature screening mechanism based on motion prediction is proposed to filter the visual features that will escape the camera perspective in advance; 3) use the adaptive adjustment module for front-end control and efficiently allocate restricted computing resources. Our approach achieves advanced estimation performance on the Euroc MAV datasets.

keywords： visual-inertial odometry attention-anticipation mechanism self-adaptive control FAST

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