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.

翻译：在面向目标检测与定位的传感器融合与状态估计领域，确保动态环境中的精确跟踪面临重大挑战。当测量值间歇性中断时，卡尔曼滤波器等传统方法常会失效，导致状态估计迅速发散。为解决此问题，我们提出了SMART（传感器测量增强与重捕获跟踪器），这是一种新颖的方法，利用卡尔曼滤波器的高频状态估计来引导对新测量值的搜索，即使在直接测量失效时也能保持跟踪的连续性。这对于传统方法难以应对的动态环境至关重要。我们的贡献包括：1）利用卡尔曼滤波器反馈的通用测量增强系统：我们实现了一个通用的测量增强系统，当主要目标检测器间歇性失效时，该系统可作为备用方案。该系统可适配多种传感器，本文以深度相机为例进行了演示，将卡尔曼滤波器的三维预测投影至二维深度图像坐标，并集成了简化为一阶近似的非线性协方差传播技术。2）开源ROS2实现：我们提供了SMART-TRACK框架的开源ROS2实现，并在使用Gazebo和ROS2构建的真实仿真环境中进行了验证，以促进更广泛的采用和进一步研究。我们的结果表明，跟踪稳定性得到显著提升，在测量中断期间估计均方根误差可低至0.04米，从而增强了无人机跟踪的鲁棒性，并拓展了在复杂场景中实现可靠自主无人机操作的潜力。实现代码发布于 https://github.com/mzahana/SMART-TRACK。