Autonomous robots in unknown indoor environments require both reliable collision avoidance and object-level understanding. Classical representations such as TSDF support safe planning but lack semantics, while photorealistic methods like Gaussian Splatting (GS) provide rich appearance yet suffer from soft geometry, limiting precise obstacle avoidance. We present LiftNav, a hybrid navigation framework built on GSFusion's TSDF+GS dual map, augmented with a real-time pipeline of YOLO-based detection, TSDF-based 3D lifting, and B-spline trajectory optimization. This design enables flexible semantic navigation without dense 3D embeddings. We further introduce a hinge-loss-based collision penalty that improves trajectory smoothness and safety. We evaluate our approach in a simulation using the Replica dataset. Compared against a state-of-the-art radiance field baseline we show a 100% feasibility rate and shorter trajectories.

翻译：自主机器人在未知室内环境中需要同时具备可靠的碰撞规避能力与物体级理解能力。经典表征方法如TSDF虽支持安全规划但缺乏语义信息，而基于高斯点云（GS）的逼真渲染方法虽能提供丰富外观信息，却因几何结构软约束而难以实现精确避障。本文提出LiftNav混合导航框架，该框架基于GSFusion的TSDF+GS双地图构建，并集成了基于YOLO的实时检测、TSDF三维语义提升以及B样条轨迹优化流水线。该设计无需稠密三维嵌入即可实现灵活的语义导航。我们进一步引入基于铰链损失的碰撞惩罚项，有效提升了轨迹平滑度与安全性。在Replica数据集仿真实验中，与最先进的辐射场基线方法相比，本方法实现了100%的规划可行性与更短的轨迹长度。