融合语义与几何的导航新方法，提升机器人在未知环境中的避障与理解能力。
LiftNav: Path Planning via Semantic Lifting in TSDF-Guided Gaussian Splatting
- 用YOLO检测+TSDF升维实现语义物体3D定位，不依赖密集点云
- 在Replica数据集上实现100%路径可行性且轨迹更短
- 适合做室内自主导航的算法研究者和工程师
未知室内环境中自主机器人的可靠避障与对象级理解至关重要。传统表示如TSDF支持安全规划但缺乏语义，而摄影级方法如高斯点阵（GS）虽具丰富外观却存在几何模糊，限制精确避障。本文提出LiftNav，基于GSFusion的TSDF+GS双地图构建的混合导航框架，集成实时的YOLO检测、基于TSDF的3D语义升维及B样条轨迹优化。该设计无需密集3D嵌入即可实现灵活语义导航。此外引入基于铰链损失的碰撞惩罚项，提升轨迹平滑性与安全性。在Replica数据集的仿真中评估表明，相比前沿辐射场基线，本方法达到100%路径可行性且轨迹更短。
原文摘要 · Abstract (English)
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.
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