Multi-Camera Pose Estimation for Mobile Robot Depth Accuracy
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Indirect time-of-flight sensors used in camera systems of mobile robots often produce inaccurate depth information due to multi-path artifacts in environments with multiple interacting surfaces, leading to incorrect object pose estimates and suboptimal grasping performance.
Innovation Solution
The method involves acquiring images from multiple perspectives using camera modules with overlapping fields of view, processing these images with machine learning models to detect sparse features, and generating a refined 3D object pose estimate using a cost function that minimizes reprojection and pitch errors, while accounting for occlusions and partial occlusions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If indirect time-of-flight sensors are used to detect depth information, then the robot can sense depth in environments with multiple interacting surfaces, but multi-path artifacts distort the measured phase and result in incorrect object pose estimates
Solution Approach 1:
The patent transitions from 2D image data to 3D spatial reconstruction by using multiple camera modules with different baselines. By capturing images from multiple perspectives and triangulating feature points in 3D space, the system overcomes the limitations of single-sensor depth measurement and eliminates multi-path artifact distortion through geometric verification across dimensions.
Solution Approach 2:
The patent introduces sparse feature points as intermediaries between the camera system and the object. Instead of directly measuring depth from the sensor to the object surface (which is corrupted by multi-path effects), the system detects feature points on the object, tracks them across multiple views, and uses these features as reliable intermediaries to compute accurate 3D pose information.
2Measurement precision
If multiple camera modules are used to capture images from different perspectives, then the robot can reduce multi-path artifacts through multi-view geometry, but the device complexity increases
Solution Approach 1:
The patent divides the imaging task across multiple camera modules, each capturing a portion of the scene from a different baseline position. By segmenting the overall pose estimation problem into multiple view-specific feature detection and matching sub-tasks, the system achieves robust 3D reconstruction while keeping each individual camera module relatively simple.
Solution Approach 2:
The patent uses multiple camera modules as copies of the same sensing mechanism positioned at different locations. Each camera module replicates the imaging function, and by combining the redundant information from these copies through multi-view geometry and feature matching, the system achieves higher measurement precision without requiring each individual sensor to be overly complex.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach significantly reduces multi-path artifacts, improving the accuracy of object pose estimation and enhancing the robot's ability to grasp objects effectively by correcting for distortions in depth measurements.
Implementation Method 1
Indirect time-of-flight sensors may be used to detect the depth information by emitting amplitude-modulated signals and measuring the phase of signals reflected from objects in the environment
Implementation Method 2
the emitted signals may be reflected from multiple surfaces thereby distorting the measured phase
Data Source
AI summary
Methods and apparatus for determining a pose of an object sensed by a camera system of a mobile robot are described. The method includes acquiring, using the camera system, a first image of the object from a first perspective and a second image of the object from a second perspective, and determining, by a processor of the camera system, a pose of the object based, at least in part, on a first set of sparse features associated with the object detected in the first image and a second set of sparse features associated with the object detected in the second image.