NOVA：面向无结构GPS拒止环境中高速目标跟踪的以对象为中心的视觉自主导航
NOVA: Navigation via Object-Centric Visual Autonomy for High-Speed Target Tracking in Unstructured GPS-Denied Environments
AI总结：
NOVA提出一种仅用立体相机和IMU的机载对象中心框架，在目标参考系中融合检测、深度补全、视觉惯性估计与NMPC，并利用控制障碍函数实现避障，在GPS拒止无结构环境中实现超过50 km/h的鲁棒高速目标跟踪。
摘要：
在无结构且GPS拒止的环境中进行自主空中目标跟踪仍然是机器人学中的一个根本性挑战。许多现有方法依赖运动捕捉系统、预先建图的场景或基于特征的定位来保证安全与控制，这限制了它们在真实世界条件下的部署。我们提出NOVA，一个完全机载的、以对象为中心的框架，仅使用一个立体相机和一个IMU即可实现鲁棒的目标跟踪和碰撞感知导航。NOVA不构建全局地图，也不依赖绝对定位，而是完全在目标的参考系中构建感知、估计和控制。一个紧密集成的系统将轻量级目标检测器与立体深度补全相结合，随后采用基于直方图的滤波在遮挡和噪声下推断鲁棒的目标距离。这些测量结果输入到一个视觉惯性状态估计器中，该估计器恢复机器人相对于目标的完整6自由度位姿。一个非线性模型预测控制器（NMPC）在目标坐标系中规划动态可行的轨迹。为确保安全，高阶控制障碍函数由从深度中提取的一组紧凑的高风险碰撞点在线构建，从而无需地图或稠密表示即可实现实时避障。我们在具有挑战性的真实世界场景中验证了NOVA，包括城市迷宫、森林小径以及反复穿越建筑物，期间存在间歇性GPS丢失和严重光照变化，这些因素会破坏基于特征的定位。每个实验在相似条件下重复多次以评估韧性，结果表现出一致且可靠的性能。NOVA实现了超过50 km/h的敏捷目标跟随。这些结果表明，仅使用机载感知，不依赖外部定位或环境假设，在野外进行高速视觉跟踪是可行的。
英文摘要：
Autonomous aerial target tracking in unstructured and GPS-denied environments remains a fundamental challenge in robotics. Many existing methods rely on motion capture systems, pre-mapped scenes, or feature-based localization to ensure safety and control, limiting their deployment in real-world conditions. We introduce NOVA, a fully onboard, object-centric framework that enables robust target tracking and collision-aware navigation using only a stereo camera and an IMU. Rather than constructing a global map or relying on absolute localization, NOVA formulates perception, estimation, and control entirely in the target's reference frame. A tightly integrated stack combines a lightweight object detector with stereo depth completion, followed by histogram-based filtering to infer robust target distances under occlusion and noise. These measurements feed a visual-inertial state estimator that recovers the full 6-DoF pose of the robot relative to the target. A nonlinear model predictive controller (NMPC) plans dynamically feasible trajectories in the target frame. To ensure safety, high-order control barrier functions are constructed online from a compact set of high-risk collision points extracted from depth, enabling real-time obstacle avoidance without maps or dense representations. We validate NOVA across challenging real-world scenarios, including urban mazes, forest trails, and repeated transitions through buildings with intermittent GPS loss and severe lighting changes that disrupt feature-based localization. Each experiment is repeated multiple times under similar conditions to assess resilience, showing consistent and reliable performance. NOVA achieves agile target following at speeds exceeding 50 km/h. These results show that high-speed vision-based tracking is possible in the wild using only onboard sensing, with no reliance on external localization or environment assumptions.
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