Underwater Vehicle Camera Rotation for Fault-Proof Imagery Collection
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Solution Overview
Problem
Underwater image and video surveys face challenges in achieving accurate and complete mapping due to high computational intensity of SLAM processes, leading to gaps and errors in data collection, especially in dynamic environments like the ocean floor.
Innovation Solution
A system and method utilizing an underwater vehicle with a central motor, rotating arms, and cameras configured to travel over a predetermined region, collecting images with guaranteed overlap and utilizing SLAM techniques for image co-registration and bundle adjustment to create a detailed topological map, employing long baseline or ultra-short baseline approaches for positioning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If SLAM processes are used to map and localize in underwater surveys, then mapping and localization capability is improved, but computational power requirement increases significantly
Solution Approach 1:
The system segments the SLAM problem into two separate components: structure-from-motion (SfM) for mapping and independent localization. Instead of running a complete 3D SLAM solution that simultaneously solves both problems, the patent uses SfM to construct 2.5D maps from image sequences and performs localization separately using image matching against the pre-built map. This segmentation reduces computational complexity while maintaining mapping and localization capabilities.
Solution Approach 2:
The patent extracts only the essential components needed for underwater survey applications rather than implementing a complete general-purpose SLAM system. Specifically, it extracts the SfM pipeline for map construction and simple image-based localization, removing complex elements like real-time particle filters and hierarchical topological representations that are not necessary for the specific application scenario.
2Device complexity
If conventional image collection methods are used, then device simplicity is maintained, but image overlap and coverage reliability deteriorate
Solution Approach 1:
The system employs dynamic camera rotation about the vehicle's forward axis during forward motion. The camera rotates at a controlled rate to sweep across the seafloor, creating overlapping image strips. This dynamic rotation, combined with forward translation, ensures guaranteed image overlap between consecutive frames without requiring complex mechanical structures or multiple fixed cameras.
Solution Approach 2:
The patent adds rotational motion in the vertical dimension (camera rotation about forward axis) to the horizontal forward motion of the vehicle. This dimensional addition transforms a simple linear translation into a spiral scanning pattern, ensuring complete coverage and guaranteed overlap between image strips while maintaining relative device simplicity.
3Loss of information
If complete 3D SLAM solution is implemented, then mapping completeness is improved, but computational intensity increases excessively
Solution Approach 1:
The patent applies partial action by implementing only the necessary portions of SLAM for the specific application. Instead of full 3D SLAM with complex pose estimation and map building, it uses 2.5D mapping via SfM which is sufficient for underwater survey applications. This partial implementation maintains mapping completeness for the intended purpose while avoiding excessive computational intensity from unnecessary 3D reconstruction algorithms.
Data Source
AI summary
An apparatus and method are presented comprising one or more sensors or cameras configured to rotate about a central motor. In some examples, the motor is configured to travel at a constant linear speed while the one or more cameras face downward and collect a set of images in a predetermined region of interest. The apparatus and method are configured for image acquisition with non-sequential image overlap. The apparatus and method are configured to eliminate gaps in image detection for fault-proof collection of imagery for an underwater survey. In some examples, long baseline (LBL) is utilized for mapping detected images to a location. In some examples, ultra-short baseline (USBL) is utilized for mapping detected images to a location. The apparatus and method are configured to utilize a simultaneous localization and mapping (SLAM) approach.