Vision-Based Drone Navigation With Hover-to-SLAM Transition
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Solution Overview
Problem
Autonomous drones face challenges in navigating after takeoff without GPS, especially in indoor environments, and require a transition to vision-based navigation.
Innovation Solution
The autonomous drone initiates navigation by capturing an image of a face or person to determine distance, then hovers to stabilize, collects images and sensor data, and transitions to a second navigation module based on visual odometry or SLAM for further navigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If GPS-based navigation is used for autonomous drone, then navigation reliability is improved, but the drone cannot operate in indoor environments where GPS is unavailable
Solution Approach 1:
The patent implements a multi-functionality principle by integrating two distinct navigation modules: a GPS-based navigation module for outdoor environments and a vision-based navigation module for indoor environments. The system can selectively activate the appropriate module based on the operational environment, ensuring reliable navigation whether GPS signals are available or not. This universal navigation capability resolves the contradiction between navigation reliability and environmental adaptability.
Solution Approach 2:
The patent applies dynamics by implementing a dynamic switching mechanism between GPS-based and vision-based navigation modules. The system continuously monitors GPS signal availability and automatically transitions between navigation modes. This dynamic adaptation allows the drone to maintain navigation reliability across varying environmental conditions, resolving the contradiction between reliable navigation and environmental versatility.
2Adaptability or versatility
If vision-based navigation is used immediately after takeoff, then environmental adaptability is improved, but navigation stability and safety deteriorate due to insufficient visual data
Solution Approach 1:
The patent implements preliminary action by requiring the drone to hover and stabilize at a predetermined location after takeoff before initiating vision-based navigation. During this hover phase, the system collects initial visual data and sensor information to establish a stable reference frame. This preliminary stabilization ensures that when vision-based navigation begins, sufficient visual data is available, maintaining navigation stability while enabling environmental adaptability.
Solution Approach 2:
The patent applies beforehand cushioning by implementing a buffer phase where the drone hovers and accumulates visual data before transitioning to active vision-based navigation. This cushioning period allows the vision system to build up sufficient data and establish reliable visual odometry or SLAM initialization, preventing navigation instability that would occur if vision-based navigation started immediately with insufficient data.
3Reliability
If the drone hovers to stabilize and collect data, then navigation stability is improved, but flight time and productivity are reduced
Solution Approach 1:
The patent implements partial action by requiring only a brief hover period sufficient to collect minimal necessary visual data and stabilize the camera, rather than extended hovering. The system determines when sufficient data has been collected and transitions to navigation, balancing the need for stability with flight efficiency. This partial action approach maintains navigation stability while minimizing productivity loss.
4Adaptability or versatility
If multiple navigation modules are integrated, then adaptability to different environments is improved, but device complexity increases
Solution Approach 1:
The patent applies the extraction principle by separating the navigation system into distinct, independent modules: a GPS-based navigation module and a vision-based navigation module. Each module is self-contained and can be independently activated. This modular extraction reduces overall system complexity compared to a fully integrated unified navigation system, as each module can be developed, tested, and maintained separately while providing environmental adaptability.
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 method allows the drone to safely navigate and capture images or data without relying on GPS, ensuring stable and effective operation in various environments.
Implementation Method 1
a second type of navigation based on visual odometry or simultaneous localization and mapping (SLAM)
Implementation Method 2
an anemometer to determine a windspeed
Data Source
AI summary
Systems, computer readable medium and methods for autonomous drone navigation based on vision are disclosed. Example methods include capturing an image using an image capturing device of the autonomous drone, processing the image to identify an object, and navigating the autonomous drone relative to the object for a period of time. After the period of time a second type of navigation is used based on determining structure from motion navigation. Images are captured during the period of time to transition to the second type of navigation. The second type of navigation uses a downward pointing navigation camera and other sensors.