Robot Pose Estimation Using Two Points and Two Lines
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing robot pose estimation methods, particularly when in close proximity to a charging station, struggle with accuracy due to insufficient image capture of charging station features, especially when fewer than four coplanar points or lines are visible, leading to inaccurate docking.
Innovation Solution
A method using two co-planar points and two co-planar lines on an object plane for robot pose estimation, combined with non-linear optimization to determine the robot's pose by comparing projected and observed errors, ensuring precise alignment within a predetermined error threshold.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional pose estimation methods using four or more coplanar points are used, then accuracy is maintained under normal conditions, but the system fails when fewer than four points are visible in close proximity to the charging station
Solution Approach 1:
The patent changes the fundamental parameters of pose estimation by transitioning from point-based methods to line-based methods. Specifically, it uses two coplanar lines with identified direction vectors instead of four or more coplanar points, fundamentally altering how geometric features are extracted and processed from the image data to achieve reliable pose estimation under limited visibility conditions
Solution Approach 2:
The patent inverts the traditional approach by using line features instead of point features for pose estimation. Rather than detecting points and inferring pose, the system detects lines, determines their direction vectors, and uses these directional information to calculate the robot's pose relative to the charging station, achieving accurate estimation even when fewer geometric features are visible
2Measurement precision
If the robot navigates closer to the charging station for precise docking, then docking precision is improved, but image capture of charging station features becomes insufficient
Solution Approach 1:
The patent transitions from two-dimensional point detection to utilizing directional information in three-dimensional space. By extracting direction vectors from line features and incorporating depth information through triangulation with the Lidar sensor, the system achieves accurate pose estimation even when the robot is in close proximity and traditional 2D image features are insufficient
Solution Approach 2:
The system changes the parameters used for pose estimation from positional coordinates of multiple points to directional parameters of lines. By using direction vectors derived from line features and combining them with depth information, the patent enables precise pose determination with minimal image capture requirements
3Ease of operation
If Lidar is used as the primary navigation sensor, then navigation capability is provided, but docking accuracy at the charging station is insufficient
Solution Approach 1:
The patent merges the capabilities of the Lidar sensor and the camera system into a unified pose estimation process. The vision system identifies line features and direction vectors from images, while the Lidar provides depth information through triangulation, and these complementary data sources are integrated to achieve both navigation capability and precise docking accuracy
Data Source
AI summary
A method for estimating a pose of a robot using two co-planar points on an object plane in the environment and two co-planar lines on the object plane, includes capturing with a camera on the robot an image of the object in the environment, including two observed points corresponding to the two co-planar points on the object and two observed lines corresponding to the two co-planar lines on the object; projecting onto the image plane the two co-planar points to obtain two projected points and projecting the two co-planar lines to obtain two projected lines; determining a point projection error by comparing the two projected co-planar points to corresponding observed points; determining a line projection error by comparing the two projected co-planar lines to corresponding observed lines; and estimating a current robot pose if the point projection error and the line projection error are below a predetermined error threshold.