Return-to-Home Feasible Micro-Aerial Vehicle Exploration for 3D Gaussian Splatting Reconstruction

2026-10-02

AI Summary

This study addresses the challenge of performing returnable indoor 3D Gaussian Splatting (3DGS) reconstruction using micro aerial vehicles under strict endurance and safety constraints. To this end, a topology-prioritized exploration framework is proposed that decouples rendering from navigation. Specifically, a sparse Voronoi skeleton is constructed via truncated signed distance fields to optimize budget-aware viewpoints. A stable roadmap extraction algorithm incorporating spatial consistency and visibility verification is introduced to suppress phantom structures, while receding horizon control with conservative collision detection ensures flight safety. Simulation results demonstrate that, given an equivalent flight budget, the proposed method outperforms or matches existing baselines in reconstruction coverage, accuracy, and return-to-home success rate.

Abstract

Micro aerial vehicles (MAVs) enable rapid indoor 3D reconstruction for inspection and time-critical situational awareness, but must operate under strict flight-time budgets and safety constraints that require explicit return-to-home (RTH) feasibility with a conservative margin. We present a topology-first active reconstruction framework that decouples high-fidelity rendering from navigation. A dense 3D Gaussian Splatting (3DGS) map is optimized as the reconstruction target, while a lightweight Truncated Signed Distance Field (TSDF)/occupancy scaffold supports conservative collision checking and online construction of a sparse 3D Voronoi skeleton roadmap. Since directly extracting roadmaps from TSDF geometry can be unstable under noisy and incomplete online fusion, we validate nodes and edges using carved free-space consistency and dense visibility checks, which suppress behind-wall phantom structure and stabilize planning. Viewpoints are selected on the roadmap using a flight-time-budget-aware objective and a lightweight receding-horizon lookahead to improve non-myopic exploration behavior. We evaluate on photorealistic indoor simulation benchmarks (ReplicaCAD, Gibson, and HM3D), reporting reconstruction coverage/error, RTH success, and computational cost. Results demonstrate improved or competitive performance relative to recent GS-based baselines under identical flight-time budgets.

Problem

• Micro Aerial Vehicle
• 3D Reconstruction
• Return-to-Home
• Exploration
• Flight-time Budget

Innovation

• 3D Gaussian Splatting
• Return-to-Home feasibility
• Voronoi skeleton roadmap
• Topology-first exploration
• Receding-horizon lookahead