Geometric Computer Vision for Real-Time Object Localization
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Solution Overview
Problem
Current systems for generating accurate localization data, such as structure from motion algorithms, are expensive, slow, and computationally intensive, requiring remote servers and being unsuitable for real-time processing by onboard edge processors in vehicles.
Innovation Solution
Implementing geometric computer vision algorithms that are less expensive, faster, and less computationally intensive, allowing for real-time localization of objects like traffic signs and street signs using edge processors onboard vehicles, by reconstructing three-dimensional models from image sequences and correlating them with GPS data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If structure from motion algorithms are used to generate localization data, then measurement precision is improved, but device complexity and computational cost increase
Solution Approach 1:
The patent segments the complex structure from motion problem into distinct functional modules: image capture, feature detection, point tracking, camera pose estimation, triangulation, and bundle adjustment. Each module performs a specific computational task, allowing the system to manage complexity through modular processing while maintaining accurate localization results.
Solution Approach 2:
The system performs preliminary actions by pre-identifying and tracking points of interest across image sequences before final localization computation. Bounding boxes are pre-established around objects, and points within these boxes are tracked through multiple frames, preparing data structures in advance to streamline the subsequent pose estimation and triangulation processes.
2Measurement precision
If structure from motion algorithms are used, then measurement precision is improved, but processing speed decreases
Solution Approach 1:
The patent implements periodic action through frame-based processing where images are captured at regular intervals and processed in sequence. The system periodically updates camera pose estimates and triangulates point positions based on new image data, enabling continuous localization updates at a sustainable processing rate rather than attempting exhaustive computation on every available data point.
Solution Approach 2:
The system applies partial action by selecting and processing only the most relevant points within bounding boxes rather than analyzing every pixel or feature in the image. This selective approach processes a subset of critical data points sufficient for accurate localization while significantly reducing the total computational burden compared to full-image processing.
3Measurement precision
If structure from motion algorithms are used, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The system performs preliminary tracking of points across multiple frames before final localization is required. By continuously monitoring and recording point positions in advance, the system prepares triangulation data structures ahead of time, so that when localization results are needed, the computation can proceed quickly using pre-processed information rather than starting from raw images.
Solution Approach 2:
The patent maintains continuity of useful action by continuously capturing images and tracking points throughout the observation period. Rather than intermittently processing data, the system continuously accumulates positional information for all tracked points, ensuring that localization computations always have current, relevant data available, reducing idle time and computational delays.
4Measurement precision
If structure from motion algorithms are used, then measurement precision is improved, but ease of operation deteriorates
Solution Approach 1:
The system implements self-service by automatically performing feature detection, point tracking, and localization computations without requiring manual intervention. The algorithm autonomously identifies objects in images, tracks their features through sequences, estimates camera poses, and generates localization data, making the complex process transparent and accessible to users who simply need results rather than computational expertise.
Solution Approach 2:
The patent uses bounding boxes as an intermediary element that simplifies the interface between object detection and detailed point tracking. The bounding box automatically encloses objects of interest and defines the region where points will be tracked, serving as a mediator that translates complex image data into structured tracking targets without requiring manual point specification or complex user configuration.
Data Source
AI summary
Systems and methods to localize objects for mapping applications may comprise a vehicle having an imaging device, a location sensor, and an edge processor. Using imaging data from the imaging device, location data from the location sensor, and bounding box data associated with objects, three-dimensional models of environments may be reconstructed using structure from motion algorithms and/or direct triangulation algorithms. After aligning the reconstructions to real-world environments based on the location data, objects may be accurately localized relative to real-world environments.