ASIP-Planner: Adaptive Planning for UAV Surface Inspection in Partially Known Indoor Environments

• URL: http://arxiv.org/abs/2605.11119v1
• Date: Mon, 11 May 2026 18:28:16 GMT
• Title: ASIP-Planner: Adaptive Planning for UAV Surface Inspection in Partially Known Indoor Environments
• Abstract summary: This work presents an adaptive UAV inspection framework for partially known structured indoor environments.<n>The proposed method integrates a segment-based global coverage planner with an inspection-oriented local view-angle adaptation module.
• Score: 7.773646942811247
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Indoor infrastructure inspection, such as tunnels and industrial facilities, requires systematic surface coverage to ensure that all inspection targets are properly observed. Unmanned Aerial Vehicles (UAVs) offer an alternative to manual inspection by conducting map-guided surface inspection using prior structural models. However, in practice, indoor inspection often relies on floorplan-derived reference maps that may not reflect unforeseen obstacles, such as temporary structures or equipment, leading to occluded viewpoints and degraded inspection quality. Existing coverage planning methods typically assume a fully known inspection environment and perform deterministic global viewpoint optimization based on accurate prior maps, making them vulnerable to environmental discrepancies during execution. This work presents an adaptive UAV inspection framework for partially known structured indoor environments. The proposed method integrates a segment-based global coverage planner with an inspection-oriented local view-angle adaptation module. The global planner organizes planar inspection targets into surface-aligned clusters to generate compact viewpoint sequences with improved orientation consistency. The local planner generates collision-free trajectories and adjusts the viewing direction online to mitigate occlusion-induced coverage loss while preserving the planned trajectory structure. The simulation results across randomized scene configurations demonstrate that the proposed global planner achieves near-complete coverage while reducing trajectory length compared to representative baselines. Real-world flight experiments further validate that the framework produces usable inspection data for downstream analysis. These results indicate that the proposed framework improves inspection efficiency and adaptability in partially known structured indoor environments.

Related papers

• RailSyn: Diagnosis-Guided Image Generation for Traceable Data Completion in Railway Foreign Object Detection [17.356035320479492]
  RailSyn is a diagnosis-guided framework comprising a real-referenced Inspector and a requirement-aligned Generator.<n>The system attains a local-shell occupation of $C_gap$ to 13.64%, which measures generated coverage of real-derived completion regions.<n>Experiments show AP50--95 gains of up to 4.9 points and consistent improvements across nine mainstream detectors.
  arXiv  Detail & Related papers  (2026-08-31T12:47:28Z)
• Z-FLoc: Zero-Shot Floorplan Localization via Geometric Primitives [72.63181031215858]
  We propose a zero-shot floorplan localization method that generalizes to novel environments without any retraining.<n>Our key insight is that dominant geometric primitives are ubiquitous in human-made environments.<n> Experiments on both simulated and real-world datasets show that our approach outperforms state-of-the-art learning-based methods on unseen environments.
  arXiv  Detail & Related papers  (2026-06-03T12:14:24Z)
• See&Say: Vision Language Guided Safe Zone Detection for Autonomous Package Delivery Drones [3.719121868494767]
  See&Say is a novel framework that combines geometric safety cues with semantic perception, guided by a Vision-Language Model (VLM) for iterative refinement.<n>We show that See&Say outperforms all baselines, achieving the highest accuracy and IoU for safety map prediction.<n>These findings highlight the promise of VLM-guided segmentation-depth fusion for advancing safe and practical drone-based package delivery.
  arXiv  Detail & Related papers  (2026-04-14T20:54:31Z)
• Coverage First Next Best View for Inspection of Cluttered Pipe Networks Using Mobile Manipulators [3.0472973584577923]
  Robotic inspection of radioactive areas enables operators to be removed from hazardous environments.<n>These systems must autonomously reconstruct the unknown environment and cover its surfaces.<n>We propose a new planning approach based on next-best-view that enables simultaneous exploration and exploitation of the environment.
  arXiv  Detail & Related papers  (2026-03-17T12:59:26Z)
• OmniPlanner: Universal Exploration and Inspection Path Planning across Robot Morphologies [13.982169714862648]
  This article presents OmniPlanner, a unified planning framework for autonomous exploration and inspection across aerial, ground, and underwater robots.<n>The method integrates volumetric exploration and viewpoint-based inspection, alongside target reach behaviors within a single modular architecture.<n>The framework is validated through extensive simulation studies and field deployments in underground mines, industrial facilities, forests, submarine bunkers, and structured outdoor environments.
  arXiv  Detail & Related papers  (2026-03-04T17:02:26Z)
• Mapping-Guided Task Discovery and Allocation for Robotic Inspection of Underwater Structures [4.067893012517312]
  Task generation for underwater multi-robot inspections can be achieved without prior knowledge of existing geometry.<n>A set of tasks is generated from SLAM meshes and optimized through expected keypoint scores and distance-based pruning.<n>Results are compared to simulated Voronoi partitions and boustrophedon patterns for inspection coverage on a model of the test environment.
  arXiv  Detail & Related papers  (2026-02-02T17:51:32Z)
• Verification of Visual Controllers via Compositional Geometric Transformations [49.81690518952909]
  We introduce a novel verification framework for perception-based controllers that can generate outer-approximations of reachable sets.<n>We provide theoretical guarantees on the soundness of our method and demonstrate its effectiveness across benchmark control environments.
  arXiv  Detail & Related papers  (2025-07-06T20:22:58Z)
• NOVA: Navigation via Object-Centric Visual Autonomy for High-Speed Target Tracking in Unstructured GPS-Denied Environments [56.35569661650558]
  We introduce NOVA, a fully onboard, object-centric framework that enables robust target tracking and collision-aware navigation.<n>Rather than constructing a global map, NOVA formulates perception, estimation, and control entirely in the target's reference frame.<n>We validate NOVA across challenging real-world scenarios, including urban mazes, forest trails, and repeated transitions through buildings with intermittent GPS loss.
  arXiv  Detail & Related papers  (2025-06-23T14:28:30Z)
• UAS-based Automated Structural Inspection Path Planning via Visual Data Analytics and Optimization [1.1496057626375067]
  Unmanned Aerial Systems (UAS) have gained significant traction for their application in infrastructure inspections. One of the core problems in this regard is electing an optimal automated flight path. This paper presents an effective formulation for the path planning problem in the context of structural inspections.
  arXiv  Detail & Related papers  (2023-12-22T23:07:20Z)
• Test-time Adaptation with Slot-Centric Models [63.981055778098444]
  Slot-TTA is a semi-supervised scene decomposition model that at test time is adapted per scene through gradient descent on reconstruction or cross-view synthesis objectives. We show substantial out-of-distribution performance improvements against state-of-the-art supervised feed-forward detectors, and alternative test-time adaptation methods.
  arXiv  Detail & Related papers  (2022-03-21T17:59:50Z)
• Differentiable Spatial Planning using Transformers [87.90709874369192]
  We propose Spatial Planning Transformers (SPT), which given an obstacle map learns to generate actions by planning over long-range spatial dependencies. In the setting where the ground truth map is not known to the agent, we leverage pre-trained SPTs in an end-to-end framework. SPTs outperform prior state-of-the-art differentiable planners across all the setups for both manipulation and navigation tasks.
  arXiv  Detail & Related papers  (2021-12-02T06:48:16Z)
• Unsupervised Domain Adaptation for Spatio-Temporal Action Localization [69.12982544509427]
  S-temporal action localization is an important problem in computer vision. We propose an end-to-end unsupervised domain adaptation algorithm. We show that significant performance gain can be achieved when spatial and temporal features are adapted separately or jointly.
  arXiv  Detail & Related papers  (2020-10-19T04:25:10Z)

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.