Binocular Vision Localization with IMU Fusion for Stable Pose Estimation
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Solution Overview
Problem
Current SLAM technologies face challenges in pose estimation accuracy and real-time performance, particularly due to environmental factors affecting cameras and the inefficiency of existing data processing algorithms.
Innovation Solution
A binocular vision localization method that combines a binocular camera unit with an inertial measurement unit, using a general graph optimization algorithm to calculate pose change information and reduce errors, allowing for stable pose estimation even in challenging environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a camera is used to collect information in SLAM, then the system can perform visual mapping and localization, but the pose estimation accuracy deteriorates when affected by environmental factors such as lights, white walls, and desktops
Solution Approach 1:
The patent combines a binocular camera unit with an inertial measurement unit (IMU) to create a hybrid vision-inertial SLAM system. The IMU provides complementary motion information that compensates for camera failures in challenging environments, thereby maintaining pose estimation accuracy when visual features are insufficient or misleading
Solution Approach 2:
The inertial measurement unit acts as an intermediary that bridges the gap when visual information becomes unreliable. The IMU data serves as a mediator to provide continuous pose estimation when the camera cannot reliably track features due to environmental factors like white walls or poor lighting
2Measurement precision
If traditional rear-end data processing algorithms are used in SLAM, then the system can perform position and posture calculation, but the real-time performance deteriorates in application scenarios with high requirements
Solution Approach 1:
The patent segments the data processing into modular components: feature extraction from binocular images, inertial parameter processing from IMU, reprojection error calculation, and graph optimization. This segmentation allows for optimized processing pipelines that can run in real-time while maintaining accuracy
Solution Approach 2:
The patent employs graph optimization algorithms that dynamically adjust optimization parameters and convergence criteria to balance computation time and accuracy. By changing optimization parameters adaptively, the system achieves real-time performance while maintaining high precision in pose estimation
3Device complexity
If only binocular camera unit is used for pose estimation, then the device complexity is reduced, but the stability deteriorates when the camera is affected by ambient noise or located in regions with less texture feature
Solution Approach 1:
The patent merges the binocular camera unit with an inertial measurement unit to create a redundant sensing system. This combination ensures that when the camera struggles with ambient noise or textureless environments, the IMU provides stable motion information to maintain reliable pose estimation
Data Source
AI summary
A binocular vision localization method, device and system are provided. The method includes calculating first pose change information according to two frames of images collected by a binocular camera unit at two consecutive moments and calculating second pose change information according to inertia parameters collected by an inertial measurement unit between the two consecutive moments. Matched feature points in the two frames are extracted from the two frames respectively. A reprojection error of each feature point is calculated. The calculations are taken as nodes or edges of a general graph optimization algorithm to acquire optimized third pose change information for localization. The system includes a binocular vision localization device, and a binocular camera unit and an inertial measurement unit respectively connected thereto, a left-eye camera and a right-eye camera are symmetrically located on two sides of the inertial measurement unit. This can improve accuracy and real-time performance for pose estimation.