This paper presents an image-feature-aware(IFA)planner for quadrotors,which integrates image feature tracking into its path-planning framework.The IFA-planner aims to improve the visual localization performance of qua...This paper presents an image-feature-aware(IFA)planner for quadrotors,which integrates image feature tracking into its path-planning framework.The IFA-planner aims to improve the visual localization performance of quadrotors in multifarious environments where feature points may be sparse or diverse.Unlike traditional methods that decouple visual localization and path planning,the IFA-planner adaptively identifies and tracks feature-rich spatial units,called anchors,along a feasible path.The anchors provide additional feature points to the visual localization module,especially in scenarios with sparse or uneven features,thus enhancing localization robustness.Via clustering-based method,the anchor selection can handle different feature point distributions without manual tuning.Moreover,a detachment prediction mechanism is incorporated to convert the selected anchors into yaw constraints and update them according to the quadrotor’s predicted state.This mechanism ensures the environmental adaptability of the anchors and avoids sudden feature changes.The effectiveness of the IFA-planner is demonstrated in simulation experiments.展开更多
基金supported by the National Key R&D Program of China(2022YFB3902801)the Fundamental Research Funds for the Central Universities(No.JUSRP123063)111 Project(B23008).
文摘This paper presents an image-feature-aware(IFA)planner for quadrotors,which integrates image feature tracking into its path-planning framework.The IFA-planner aims to improve the visual localization performance of quadrotors in multifarious environments where feature points may be sparse or diverse.Unlike traditional methods that decouple visual localization and path planning,the IFA-planner adaptively identifies and tracks feature-rich spatial units,called anchors,along a feasible path.The anchors provide additional feature points to the visual localization module,especially in scenarios with sparse or uneven features,thus enhancing localization robustness.Via clustering-based method,the anchor selection can handle different feature point distributions without manual tuning.Moreover,a detachment prediction mechanism is incorporated to convert the selected anchors into yaw constraints and update them according to the quadrotor’s predicted state.This mechanism ensures the environmental adaptability of the anchors and avoids sudden feature changes.The effectiveness of the IFA-planner is demonstrated in simulation experiments.
