Vehicle Self-Localization Fault Sensing With Dual Lidar Trajectories
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Solution Overview
Problem
Existing GPS self-location estimation systems in vehicles, such as mining dump trucks, face challenges in detecting anomalies in areas with unknown terrain geometries, leading to errors due to factors like multipath interference, and require pre-recorded terrain information for accurate positioning.
Innovation Solution
An anomaly detection system using two lidars installed on a vehicle to detect feature points and calculate trajectories of these points, allowing for real-time anomaly detection without pre-obtained terrain information, by comparing the trajectories from both lidars and adjusting self-location estimates based on GPS and inertial data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If terrain information is pre-obtained and recorded for anomaly detection, then anomaly detection accuracy is improved, but the system cannot operate in areas with unknown terrain geometries
Solution Approach 1:
The system performs preliminary actions by having the first measuring device (front lidar) detect feature points and generate trajectory data before the second measuring device (rear lidar) uses this information for anomaly detection. This allows the system to build reference data dynamically during operation, enabling both accuracy and adaptability to unknown terrains.
Solution Approach 2:
The patent introduces trajectory data as an intermediary element between the measuring devices and the anomaly detection process. The trajectory, generated from feature point data collected by the first measuring device, serves as a reference mediator that enables the second measuring device to detect anomalies without requiring pre-stored terrain information, thus resolving the contradiction between detection accuracy and adaptability to unknown environments.
2Measurement precision
If GPS positioning data is used for self-location estimation, then positioning capability is provided, but errors occur due to multipath interference in complex terrain
Solution Approach 1:
The system implements feedback by continuously comparing the trajectory data from the first measuring device with the positioning data from the second measuring device. When discrepancies are detected, the system uses this feedback to determine anomalies and correct positioning errors, thereby improving reliability while maintaining GPS positioning capability.
Solution Approach 2:
The patent substitutes the purely GPS-based mechanical positioning system with an enhanced system that incorporates optical measurement (lidar) and inertial measurement. This substitution creates a hybrid positioning approach that is less susceptible to multipath interference, improving reliability in complex terrain while maintaining the original GPS positioning capability.
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
Enables accurate detection of self-location anomalies in vehicles, even in areas with varying terrain, by compensating for GPS errors and maintaining accurate navigation without pre-recorded terrain data, thus enhancing vehicle control and safety.
Implementation Method 1
a front Lidar 2a and a rear Lidar 2b as sensors that detect a relative position between the dump truck 10 and the surrounding structure
Data Source
Figure 1
Figure 2
Figure 3(a)~3(b)
AI summary
A first measuring device measures a location of its own vehicle relative to each feature point on a surrounding structure around a road surface on which the vehicle. A first feature-point is provided which, based on output from the first measuring device, acquires coordinates of the each feature point expressed in a coordinate system of the first measuring device, and uses a self-location to convert the coordinates into an external coordinate. A feature-point trajectory generation section is provided to generate, based on the first coordinates, a trajectory of the feature point group. A second measuring device measures a location of its own vehicle relative to a feature point located rearward of the feature point measured by the first measuring device. An anomaly determination section is provided to determine that anomaly occurs in a self-location estimation device if the second coordinates are on the trajectory.