Mobile Robot 3D Modeling Path Planning for Complete Surface Coverage
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Solution Overview
Problem
Existing multi-view stereo (MVS) algorithms for 3D modeling are inefficient due to repeated scanning, incomplete coverage, and slow processing times, especially in textureless scenes and with occlusions, and do not guarantee accurate reconstruction results.
Innovation Solution
A mobile robot control apparatus with an online 3D modeler and path planner that generates and optimizes view paths in real time, using volumetric and surfel maps to identify target surfaces and optimize camera trajectories for improved reconstruction quality without rescanning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the explore-then-exploit method is used to acquire images for MVS, then image acquisition is performed in two stages, but the initial and rescanning trajectories frequently overlap leading to inefficient time performance
Solution Approach 1:
The patent implements dynamic trajectory adjustment by continuously updating the camera path based on real-time reconstruction quality assessment. The system transitions from static pre-planned trajectories to dynamic adaptive paths that respond to actual scene characteristics, avoiding previously scanned regions and focusing on unreconstructed areas.
Solution Approach 2:
The system employs feedback mechanisms where reconstruction quality metrics from the MVS algorithm are continuously monitored and used to adjust subsequent image acquisition trajectories. The controller receives feedback about which regions are adequately reconstructed and modifies the camera path to prioritize under-reconstructed areas, eliminating the need for redundant rescanning.
2Measurement precision
If MVS algorithms are used to model large-scale structures, then 3D reconstruction is achieved, but the algorithms take a long time to process images making the entire modeling process extremely slow
Solution Approach 1:
The system performs preliminary actions by pre-processing and selecting only the most informative image pairs for MVS reconstruction before full processing. The controller identifies and prioritizes critical viewpoints that contribute most to reconstruction quality, preparing optimization parameters in advance to reduce computational burden during actual processing.
Solution Approach 2:
The patent applies partial action by selectively processing only the most critical image subsets rather than all acquired images through the full MVS pipeline. The system identifies key baseline distances and selects representative image pairs that provide sufficient reconstruction information with reduced computational effort.
3Measurement precision
If the explore-then-exploit method rescans unreconstructed regions from the coarse model, then attempt to improve coverage, but the frequent overlap of trajectories leads to inefficient performance
Solution Approach 1:
The system uses feedback from the coarse model assessment to dynamically adjust the rescanning trajectory. Instead of following fixed rescanning paths, the controller continuously monitors which regions remain under-reconstructed and adapts the camera path in real-time to target only those specific areas, eliminating redundant overlap with already scanned regions.
Solution Approach 2:
The patent transforms the static rescanning phase into a dynamic adaptive process where the trajectory is continuously modified based on real-time evaluation of reconstruction completeness. The system responds to changing scene understanding and model quality metrics to optimize the rescanning path on-the-fly.
4Measurement precision
If MVS algorithms process all acquired images, then complete coverage is achieved, but textureless scenes, short baseline distances, and occlusions still prevent accurate reconstruction
Solution Approach 1:
The system performs preliminary assessment of scene characteristics before full MVS processing. The controller evaluates baseline distances, detects occlusion patterns, and identifies textureless regions in advance, then pre-configures processing parameters and selects optimal image subsets tailored to these specific challenges.
Solution Approach 2:
The patent dynamically adjusts MVS algorithm parameters based on detected scene characteristics. For textureless scenes, it modifies matching thresholds and feature detection sensitivity. For short baselines, it adjusts triangulation parameters. For occlusions, it changes visibility assessment criteria, allowing accurate reconstruction without processing all images uniformly.
Data Source
AI summary
A mobile robot control apparatus includes an online 3D modeler and a path planner. The online 3D modeler is configured to receive an image sequence from a mobile robot and to generate a first map and a second map different from the first map based on the image sequence. The path planner is configured to generate a global path based on the first map, to extract a target surface based on the second map and to generate a local inspection path having a movement unit smaller than a movement unit of the global path based on the global path and the target surface.