Polyhedral Geofences for Lidar Navigation in GPS-Denied Areas
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Solution Overview
Problem
The intermittency of GPS at low altitudes and in urban/industrial environments, combined with the high data volume of high-fidelity lidar maps, poses a computational challenge for real-time processing in vehicles, limiting their ability to navigate and avoid obstacles effectively.
Innovation Solution
Generating polyhedral geofences from high-fidelity lidar data, which simplifies high-density point clouds into 2.5D or 3D polyhedrons, allowing for significant data reduction and enabling real-time processing and navigation, even in GPS-degraded environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-fidelity lidar maps are used for navigation and obstacle avoidance, then measurement precision and reliability are improved, but data volume increases making real-time processing difficult
Solution Approach 1:
The patent segments the continuous point cloud data into discrete polyhedral geofences. Each polyhedral geofence represents a specific obstacle or terrain feature, dividing the large dataset into manageable geometric primitives that can be processed efficiently for navigation and collision avoidance.
Solution Approach 2:
The patent transforms point cloud data from its original high-dimensional format into a simplified parameterized representation using polyhedral geofences. This parameter change reduces data complexity while preserving essential spatial information needed for position determination and obstacle avoidance.
2Reliability
If high-fidelity lidar maps are used for real-time navigation, then reliability is improved, but processing speed decreases due to computational complexity
Solution Approach 1:
By segmenting the environment into discrete polyhedral geofences, the system enables faster spatial queries and collision detection algorithms. The segmented representation allows for efficient nearest-neighbor searches and geometric intersection tests compared to processing raw point clouds.
Solution Approach 2:
The patent creates a simplified geometric copy (polyhedral geofence) that represents the essential features of the original complex lidar data. This copy retains sufficient information for navigation and obstacle avoidance while enabling real-time processing speeds.
3Ease of operation
If GPS is used for position determination at low altitudes and in urban environments, then ease of operation is improved, but measurement precision deteriorates due to signal intermittency
Solution Approach 1:
The patent introduces polyhedral geofences as an intermediary reference system between the vehicle and GPS satellites. By matching observed terrain features against pre-built polyhedral geofences, the system achieves accurate position determination in GPS-denied environments without requiring direct satellite signals.
Solution Approach 2:
The polyhedral geofence system serves multiple functions: it provides position determination, obstacle avoidance, and navigation guidance. This multi-functional approach replaces the need for GPS while maintaining navigation simplicity and improving accuracy in urban environments.
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 reduces data volume by orders of magnitude, enabling real-time obstacle avoidance and navigation, exceeding GPS accuracy near ground structures and allowing vehicles to safely inspect and maneuver around complex environments.
Implementation Method 1
an onboard light detection and ranging (lidar) sensor used to map ground structures
Data Source
AI summary
Exemplary methods and systems may generate a point cloud data generated polyhedral geofence for use in navigating a vehicle. The polyhedral geofence may be generated, or created, from point cloud data such as lidar data. Further, a vehicle may utilize propulsion devices and controllers for moving the vehicle based on a point map data generated polyhedral geofence.