Drone LIDAR Reconstruction With Cloud SLAM Drift Correction
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Solution Overview
Problem
Existing unmanned aerial vehicles (UAVs) face inaccuracies in GPS positioning, leading to poor accuracy and completeness in 3D model reconstruction, especially in obstructed settings, and the computational limitations of drones hinder efficient trajectory planning and SLAM algorithms, resulting in high SLAM error and battery exhaustion during large-area scans.
Innovation Solution
A system utilizing a drone-mounted LIDAR with a lightweight onboard component and cloud-based processing to minimize battery usage and enhance 3D model reconstruction, employing trajectory planning, feature detection, and real-time drift correction, enabling near-real-time, accurate 3D model construction of large outdoor spaces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If GPS positioning is used to position point clouds, then the system can operate autonomously, but the positioning accuracy deteriorates to within 4.9m radius under open sky and several meters in obstructed settings
Solution Approach 1:
The patent introduces an intermediary system consisting of ground control points (GCPs) and a post-processing computation stage. The drone captures images with embedded GPS coordinates and timestamps, then these data are processed offline by matching features across multiple images and correlating them with GCP locations. This intermediary processing chain transforms the raw GPS data into accurate 3D point cloud positions, resolving the contradiction between autonomous operation and positioning precision.
2Loss of time
If the drone carries LIDAR and computational equipment for real-time 3D reconstruction, then near-real-time modeling is achieved, but battery consumption increases
Solution Approach 1:
The patent performs preliminary actions during the flight by capturing images and GPS data with proper overlap (60-80% forward overlap, 30-60% side overlap) and embedding metadata. The heavy computational lifting is deferred to post-processing using ground-based computing resources. This allows the drone to maintain minimal onboard equipment while still achieving near-real-time reconstruction when processing occurs during or immediately after flight.
Solution Approach 2:
The patent uses ground-based computing systems as intermediaries to perform the computationally intensive SLAM and point cloud generation tasks. The drone acts only as a data collection platform, transmitting raw images and GPS data to ground stations or cloud servers for processing. This separation of computation from the moving platform significantly reduces the drone's energy consumption while maintaining reconstruction speed.
3Reliability
If the drone performs comprehensive area scanning to ensure complete coverage, then model completeness improves, but flight duration and battery usage increase
Solution Approach 1:
The patent pre-plans flight trajectories with specified overlap parameters (60-80% forward, 30-60% side) to ensure complete coverage before flight. This preliminary planning optimizes the flight path to achieve comprehensive coverage with minimal redundant flights, ensuring model completeness while minimizing flight duration and battery consumption.
Solution Approach 2:
The patent implements a feedback mechanism where the system monitors captured images and GPS data quality during flight, then adjusts or validates coverage after flight. The post-processing stage analyzes whether sufficient overlap and coverage were achieved, and can identify areas requiring re-scanning. This feedback loop ensures complete model coverage while optimizing flight efficiency by avoiding unnecessary redundant flights.
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
The system achieves sub-meter accuracy and near-real-time 3D model reconstruction with minimal battery consumption by optimizing flight trajectories, leveraging cloud computing for complex computations, and correcting SLAM drift, ensuring high-quality 3D models of buildings and environments.
Implementation Method 1
a drone-mounted LIDAR with a lightweight onboard component and cloud-based processing
Data Source
AI summary
Systems and methods for automatically constructing a 3-dimensional (3D) model of a feature using a drone. The method includes generating a reconnaissance flight path that minimizes battery usage by the drone, and conducting a discovery flight that uses the reconnaissance flight path. The method further includes transmitting reconnaissance laser sensor data from the drone to a processing system for target identification, and selecting a target feature for 3D model construction based on the reconnaissance laser sensor data. The method further includes scanning the target feature using a laser sensor, transmitting laser sensor data for the target feature having a minimum point density from the drone to the processing system for 3D model construction, and constructing the 3D model from the minimum point density laser sensor data.