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センサフュージョンarXiv:2410.10409

SMART-TRACK: カルマンフィルタ誘導センサフュージョンによる動的環境下でのロバストなUAV物体追跡

SMART-TRACK: A Novel Kalman Filter-Guided Sensor Fusion For Robust UAV Object Tracking in Dynamic Environments

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カルマンフィルタの高頻度状態推定を活用して検出器が途切れた際も追跡を継続するSMART-TRACKを提案し、ROS2でオープンソース実装してGazebo上で有効性を検証した。

著者: Khaled Gabr, Mohamed Abdelkader, Imen Jarraya, Abdullah AlMusalami, Anis Koubaa

分類: cs.RO

原文アブストラクト

In the field of sensor fusion and state estimation for object detection and localization, ensuring accurate tracking in dynamic environments poses significant challenges. Traditional methods like the Kalman Filter (KF) often fail when measurements are intermittent, leading to rapid divergence in state estimations. To address this, we introduce SMART (Sensor Measurement Augmentation and Reacquisition Tracker), a novel approach that leverages high-frequency state estimates from the KF to guide the search for new measurements, maintaining tracking continuity even when direct measurements falter. This is crucial for dynamic environments where traditional methods struggle. Our contributions include: 1) Versatile Measurement Augmentation Using KF Feedback: We implement a versatile measurement augmentation system that serves as a backup when primary object detectors fail intermittently. This system is adaptable to various sensors, demonstrated using depth cameras where KF's 3D predictions are projected into 2D depth image coordinates, integrating nonlinear covariance propagation techniques simplified to first-order approximations. 2) Open-source ROS2 Implementation: We provide an open-source ROS2 implementation of the SMART-TRACK framework, validated in a realistic simulation environment using Gazebo and ROS2, fostering broader adaptation and further research. Our results showcase significant enhancements in tracking stability, with estimation RMSE as low as 0.04 m during measurement disruptions, advancing the robustness of UAV tracking and expanding the potential for reliable autonomous UAV operations in complex scenarios. The implementation is available at https://github.com/mzahana/SMART-TRACK.

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