# LiftNav: Path Planning via Semantic Lifting in TSDF-Guided Gaussian Splatting Source: [LiftNav: Path Planning via Semantic Lifting in TSDF-Guided Gaussian Splatting]() Collection index: [llms.txt]() Data updated: 2026-06-02T05:00:12.522Z Authors: Hannah Schieber, Dominik Frischmann, Victor Schaack, Angela P. Schoellig, Daniel Roth Published: 2026-05-29T14:44:59.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Discretized Goal Navigation on Replica](): Discretized Goal Navigation; Replica; Feasibility: 100. - [Semantic Instance Targeting on Replica](): Semantic Instance Targeting; Replica; Feasibility: 100. ## Original sources and implementation [Original paper]() [PDF]()