6D Vehicle Odometry Using a Roadmodel 2D Manifold
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Solution Overview
Problem
Conventional 3D odometry in automated driving vehicles does not account for elevation changes, leading to inaccuracies when navigating slopes or curved roads, as traditional positioning techniques like GPS are not sufficient for real-time updates.
Innovation Solution
A method and apparatus that transform roadmodels from 3D (x, y, z) space into 6D (x, y, z, r, p, y) space to create a roadmodel 2D manifold, allowing for the projection of delta 3D odometry onto this manifold to determine the 6D delta pose of a vehicle, incorporating height, roll, pitch, and yaw angles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional 3D odometry is used for navigation, then the system is simple and easy to implement, but it does not account for elevation changes leading to navigation inaccuracies on slopes
Solution Approach 1:
The patent transforms the roadmodel from 3D space (x, y, z) into 6D space (x, y, z, r, p, y) by adding orientation dimensions (roll, pitch, yaw angles). This dimensional expansion allows the system to capture both position and orientation information, enabling accurate representation of elevation changes and vehicle posture on slopes while maintaining a unified manifold structure for computation.
Solution Approach 2:
The patent introduces six parameters (x, y, z for position and r, p, y for orientation angles) to fully describe the vehicle's state. By changing from 3D position-only representation to 6D pose representation, the system can accurately reflect elevation changes and vehicle posture, resolving the measurement precision issue while keeping the computational framework manageable through parameterization.
2Measurement precision
If traditional positioning techniques like GPS are used to update vehicle location, then the system complexity remains low, but the accuracy and update frequency are insufficient for automated driving
Solution Approach 1:
The patent introduces a roadmodel 2D manifold as an intermediary between the vehicle's odometry system and the navigation system. This manifold serves as a reference framework that transforms 3D odometry measurements into accurate 6D pose estimates by projecting onto the pre-defined road surface model, thereby improving positioning accuracy without requiring additional external positioning hardware.
Solution Approach 2:
The patent creates a digital copy of the road surface in 6D space that mirrors the physical road geometry including elevation changes. This virtual roadmodel manifold acts as a reference copy that can be used to correct and refine odometry measurements, providing high-accuracy positioning information without the need for complex external positioning systems.
3Measurement precision
If 2D map representation is used for road navigation, then the map structure is simple, but it cannot reflect actual road length on slopes causing navigation errors
Solution Approach 1:
The patent elevates the roadmap representation from 2D to 6D space by incorporating three positional dimensions (x, y, z) and three orientational dimensions (roll, pitch, yaw). This allows the map to represent not only horizontal positions but also elevation changes and road orientations, enabling accurate calculation of actual road lengths on slopes while maintaining a structured manifold representation for efficient computation.
Data Source
AI summary
A method for determining a 6-dimensional (6D) delta pose of a vehicle includes obtaining a pre-defined roadmodel 2-dimensional (2D) manifold in 6D space, wherein the roadmodel 2D manifold in 6D space is pre-defined by transforming objects in a roadmodel from 3D (x, y, z) space into 6D (x, y, z, r, p, y) space, wherein x and y represent positions of objects on a horizontal plane, and z, r, p and y represent the height, roll angle, pitch angle and yaw angle of the objects in real world; obtaining a delta 3D odometry (Δx, Δy, θ) from an odometer of the vehicle, wherein Δx and Δy represent the movements in the lateral and forward-reverse directions, and θ represents a current heading of the vehicle; projecting the obtained delta 3D odometry onto the roadmodel 2D manifold; and determining the 6D delta pose of the vehicle corresponding to the delta 3D odometry.