Vehicle Self-Motion Determination Using Dynamic Reference Points
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Solution Overview
Problem
Existing methods for determining a vehicle's self-motion in dynamic environments are not sufficiently quick or reliable, as they fail to effectively adapt to changing conditions and often misidentify stationary points.
Innovation Solution
A dynamic system that continuously adjusts a set of reference points using two algorithms: one for adding new stationary points and another for removing non-stationary points, employing optical flow analysis, Kalman filtering, and statistical methods to ensure accurate identification of self-motion in rapidly changing environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If optical flow analysis is used to determine stationary points, then self-motion can be determined, but reliability deteriorates in rapidly changing environments where stationary points are frequently lost or misidentified
Solution Approach 1:
The patent implements a dynamic reference point management system where the set of reference points is continuously updated. A first algorithm dynamically adds new reference points when they become stationary, and a second algorithm dynamically removes reference points when they cease to be stationary. This dynamic adaptation allows the system to maintain reliability in rapidly changing environments by ensuring the reference point set always reflects current environmental conditions.
Solution Approach 2:
The system employs feedback mechanisms through statistical checks and threshold values to monitor the properties of reference points. The optical flow of reference points is continuously evaluated, and when statistical deviations exceed predefined thresholds, the system triggers updates to the reference point set. This feedback loop ensures that the system maintains reliable self-motion determination by detecting and responding to changes in environmental conditions.
2Measurement precision
If the set of reference points is kept fixed, then computational simplicity is maintained, but measurement precision deteriorates as environmental conditions change
Solution Approach 1:
The patent transforms the static reference point set into a dynamic one that automatically adapts to environmental changes. The first algorithm adds reference points when environmental conditions create new stationary points, and the second algorithm removes reference points when they become non-stationary. This dynamic management maintains measurement precision without requiring complex manual intervention, as the system automatically adjusts the reference point set based on real-time optical flow analysis.
Solution Approach 2:
The system performs self-service through automated reference point management. The algorithms continuously monitor the optical flow of image points and automatically update the reference point set without external intervention. This self-managing approach maintains measurement precision while avoiding the complexity of manual reference point selection and management.
3Reliability
If statistical methods and threshold values are used to check reference points, then reliability improves, but computational time increases
Solution Approach 1:
The patent applies statistical checks and threshold value evaluations selectively rather than continuously to all reference points. The system monitors optical flow and triggers statistical validation only when changes exceed predefined thresholds, performing partial validation rather than exhaustive checking. This approach maintains reliability by validating reference points when necessary while reducing computational time by avoiding unnecessary continuous validation of all points.
Data Source
AI summary
A method and a device for determining the self-motion of a vehicle in an environment are provided, in which at least part of the environment is recorded via snapshots by an imaging device mounted on the vehicle. At least two snapshots are analyzed for determining the optical flows of image points, reference points that seem to be stationary from the point of view of the imaging device being ascertained from the optical flows. The reference points are collected in an observed set, new reference points being dynamically added to the observed set with the aid of a first algorithm, and existing reference points being dynamically removed from the observed set with the aid of a second algorithm.