LiftNav: Path Planning via Semantic Lifting in TSDF-Guided Gaussian Splatting

• URL: http://arxiv.org/abs/2605.31376v1
• Date: Fri, 29 May 2026 14:44:59 GMT
• Title: LiftNav: Path Planning via Semantic Lifting in TSDF-Guided Gaussian Splatting
• Abstract summary: We present LiftNav, a hybrid navigation framework built on GSFusion's TSDF+GS dual map.<n>This design enables flexible semantic navigation without dense 3D embeddings.<n>Compared against a state-of-the-art radiance field baseline we show a 100% feasibility rate and shorter trajectories.
• Score: 8.537296437078387
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Embodied-Navigator: Point, Think, Memorize, and Align for Efficient Navigation [68.02899606570223]
  We propose TAMP-Nav, a unified framework for efficient embodied navigation.<n>First, we introduce a Pixel-to-3D Action Formulation (Point) that reformulates navigation into 2D visual coordinates.<n>Second, we propose an integrated Selective Reasoning and Anchor-Trajectory Memory mechanism (Think and Memorize)
  arXiv  Detail & Related papers  (2026-08-18T08:37:08Z)
• HSDF-Lane: Height-Aligned Signed Distance Field with Semantic Lane Prior for 3D Lane Detection [42.418429168532406]
  We propose HSDF-Lane, which implicitly models the road surface as a Height-aligned Signed Distance Field (HSDF) over a densely sampled 3D feature volume.<n>HSDF-Lane achieves state-of-the-art performance in both 3D lane detection and height map estimation.
  arXiv  Detail & Related papers  (2026-06-30T06:02:59Z)
• SCAN-Planner: Spatial Collision-Aware Local Planning for Route-Guided Long-Range Quadruped Navigation [9.033597184884746]
  SCAN-Planner is a spatial collision-aware local planning framework for long-range quadruped navigation.<n>A yaw-aware twin-cylinder footprint is used to model the elongated robot body.<n>For large-scale deployment, a robot-centric sliding map with boundary fallback provides high-resolution local collision checking.
  arXiv  Detail & Related papers  (2026-06-17T19:55:09Z)
• Plan in Sandbox, Navigate in Open Worlds: Learning Physics-Grounded Abstracted Experience for Embodied Navigation [63.11032720821731]
  textitSAGE is a framework that enables agents to learn within a physics-grounded semantic abstraction rather than a photorealistic simulation.<n>We demonstrate that textitSAGE significantly improves planner-assisted embodied navigation, achieving a 53.21% LLM-Match Success Rate on A-EQA.
  arXiv  Detail & Related papers  (2026-05-11T07:34:30Z)
• SpatialFly: Geometry-Guided Representation Alignment for UAV Vision-and-Language Navigation in Urban Environments [49.966170814478915]
  We propose a geometry-guided spatial representation framework for UAV VLN.<n>Running on RGB observations without explicit 3D reconstruction, SpatialFly introduces a geometry-guided 2D representation alignment mechanism.<n> Experimental results show that SpatialFly consistently outperforms state-of-the-art UAV VLN baselines across both seen and unseen environments.
  arXiv  Detail & Related papers  (2026-03-22T03:56:58Z)
• Vision-Guided MPPI for Agile Drone Racing: Navigating Arbitrary Gate Poses via Neural Signed Distance Fields [8.66703842496129]
  Drone racing requires tight coupling of perception, planning, and control under extreme agility.<n>Recent approaches rely on precomputed spatial reference trajectories or explicit 6-DoF gate pose estimation.<n>We propose a vision guided optimal control framework that enables reference-free agile flight through arbitrarily placed and oriented gates.
  arXiv  Detail & Related papers  (2026-03-07T13:18:07Z)
• SWIFT-Nav: Stability-Aware Waypoint-Level TD3 with Fuzzy Arbitration for UAV Navigation in Cluttered Environments [0.0]
  We propose a TD3-based navigation framework that achieves fast, stable convergence to obstacle-aware paths.<n>We show that combining TD3 with replay prioritisation, exploration, and fuzzy-safety rules yields a robust and deployable solution for UAV navigation in cluttered scenes.
  arXiv  Detail & Related papers  (2025-12-17T23:19:06Z)
• RAP: 3D Rasterization Augmented End-to-End Planning [104.52778241744522]
  Imitation learning for end-to-end driving trains policies only on expert demonstrations.<n>We propose 3D Rasterization, which replaces costly rendering with lightweightization of annotated primitives.<n>RAP achieves state-of-the-art closed-loop and long-tail robustness, ranking first on four major benchmarks.
  arXiv  Detail & Related papers  (2025-10-05T19:31:24Z)
• Unified Linear Parametric Map Modeling and Perception-aware Trajectory Planning for Mobile Robotics [1.7495208770207367]
  We introduce a lightweight linear parametric map by first mapping data to a high-dimensional space, followed by a sparse random projection for dimensionality reduction.<n>For UAVs, our method grid and Euclidean Signed Distance Field (ESDF) maps.<n>For UGVs, the model characterizes terrain and provides closed-form gradients, enabling online planning to circumvent large holes.
  arXiv  Detail & Related papers  (2025-07-12T16:39:19Z)
• GaussRender: Learning 3D Occupancy with Gaussian Rendering [86.89653628311565]
  GaussRender is a module that improves 3D occupancy learning by enforcing projective consistency.<n>Our method penalizes 3D configurations that produce inconsistent 2D projections, thereby enforcing a more coherent 3D structure.
  arXiv  Detail & Related papers  (2025-02-07T16:07:51Z)
• MonoGSDF: Exploring Monocular Geometric Cues for Gaussian Splatting-Guided Implicit Surface Reconstruction [86.87464903285208]
  We introduce MonoGSDF, a novel method that couples primitives with a neural Signed Distance Field (SDF) for high-quality reconstruction.<n>To handle arbitrary-scale scenes, we propose a scaling strategy for robust generalization.<n>Experiments on real-world datasets outperforms prior methods while maintaining efficiency.
  arXiv  Detail & Related papers  (2024-11-25T20:07:07Z)
• DeSiRe-GS: 4D Street Gaussians for Static-Dynamic Decomposition and Surface Reconstruction for Urban Driving Scenes [71.61083731844282]
  We present DeSiRe-GS, a self-supervised gaussian splatting representation.<n>It enables effective static-dynamic decomposition and high-fidelity surface reconstruction in complex driving scenarios.
  arXiv  Detail & Related papers  (2024-11-18T05:49:16Z)
• Neural Signed Distance Function Inference through Splatting 3D Gaussians Pulled on Zero-Level Set [49.780302894956776]
  It is vital to infer a signed distance function (SDF) in multi-view based surface reconstruction. We propose a method that seamlessly merge 3DGS with the learning of neural SDFs. Our numerical and visual comparisons show our superiority over the state-of-the-art results on the widely used benchmarks.
  arXiv  Detail & Related papers  (2024-10-18T05:48:06Z)
• GS-ROR$^2$: Bidirectional-guided 3DGS and SDF for Reflective Object Relighting and Reconstruction [20.523085632567717]
  We propose an SDF-aided Gaussian splatting for efficient optimization of the relighting model and a GS-guided SDF enhancement for high-quality geometry reconstruction.<n>Our method can further provide high-quality meshes for reflective objects at the cost of 17% extra training time.
  arXiv  Detail & Related papers  (2024-05-22T09:40:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.