AI 中文总结
针对无人机“看见并到达”导航中语义-控制失配问题，提出视觉-语言航路点预测框架DBFly，通过空间机动决策链、隐式飞行走廊和感知终端收敛的停止策略，使成功率较SOTA基线平均提升25.07个百分点。
AI 中文摘要
无人机“看见并到达”导航要求空中智能体接近其初始视图中可见的语言指定目标，并可靠地停在目标附近。现有方法通常将视觉-语言表示直接映射到动作输出，而未明确建模中间细粒度空间决策，这种直接映射会导致语义-控制失配，进而引发不一致的机动动作和不可靠的终止。为解决该问题，我们提出DBFly，一种视觉-语言航路点预测框架，在航路点生成前引入显式视觉引导空间深思熟虑。具体而言，DBFly引入空间机动决策链，逐步执行目标方向锚定、空间诊断和机动决策，使高层机动意图能显式引导连续航路点生成；DBFly还通过将初始目标方向先验转换为持久几何参考，并从无人机当前位置推导在线走廊状态，构建隐式飞行走廊，为空间诊断和机动修正提供软几何引导；此外，DBFly开发了感知终端收敛的停止策略，通过目标接近度和短 horizon 运动收敛共同表征终端状态，实现更可靠的目标附近停止。在可见对象、未见对象和未见场景测试集上的大量实验表明，DBFly较SOTA基线的成功率平均提升25.07个百分点，项目主页可通过此https URL获取。
英文摘要
UAV see-and-reach navigation requires an aerial agent to approach a language-specified target visible in its initial view and stop reliably near it. Existing methods typically map vision-language representations directly to action outputs without explicitly modeling intermediate fine-grained spatial decisions. This direct mapping causes semantic-control misalignment, leading to inconsistent maneuvers and unreliable termination. To address this issue, we propose DBFly, a vision-language waypoint prediction framework that introduces explicit vision-guided spatial deliberation before waypoint generation. Specifically, DBFly introduces a spatial maneuver decision chain that progressively performs target-direction anchoring, spatial diagnosis, and maneuver decision, enabling high-level maneuver intent to explicitly guide continuous waypoint generation. DBFly further constructs an implicit flight corridor by transforming the initial target-direction prior into a persistent geometric reference and deriving an online corridor state from the UAV's current position, thereby providing soft geometric guidance for spatial diagnosis and maneuver correction. In addition, DBFly develops a terminal-convergence-aware stopping strategy that characterizes terminal states through both target proximity and short-horizon motion convergence, enabling more reliable stopping near the target. Extensive experiments across seen, unseen-object, and unseen-scene test sets demonstrate that DBFly improves the success rate over the SOTA baseline by an average of 25.07 percentage points. The project homepage is available at https://xuefanfu.github.io/DBFly-Page.
Comments13 pages, 9 figures
