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A Singapore institute has developed a vision-based navigation solution to aid indoor navigation in Global Positioning Systems (GPS) denied environments, eliminating the need for expensive and weighty sensors such as Light Detection and Ranging (LIDAR) which are commonly used in most indoor autonomous drones and Autonomous Ground Vehicles (AGV).
The institute seeks licensing/commercial partnerships with technical assistance with MNEs/SMEs of all sizes.
Drones are increasingly being used in outdoor environments, based on GPS as the key technology for localisation and mapping in autonomous flights. However, the technology for autonomous drone systems used for indoor localisation and mapping is still maturing, with most of the systems still under research and development.
Existing autonomous indoor systems rely on expensive sensors that are costly to operate for commercial businesses. As GPS is unavailable indoors, these sensors which include lasers, sonars or computer vision, are used as navigation tools to determine location. Besides being costly, the autonomous systems that rely on sensors are heavy in weight and/or have demanding processing requirements.
The cost-effective and lighter solution, the Semantic Depth Prediction System (SDPS) developed by the Singapore institute, uses a monocular camera to recreate a 3D scene by fusing object detection, semantic segmentation and depth estimation. The use of a monocular camera reduces the cost and size of the indoor drone, thus ensuring safer operations in confined indoor spaces.
Besides being used as a standalone system, this solution can be integrated with other available sensors to provide a more robust navigation solution for indoor operations.
The system's overall architecture is based on an open source machine learning library for research and production, and is extendable for future enhancements.
The navigation software is in the form of modular Application Programming Interfaces (APIs). This software can support any drones with specific flight computers that possess navigation functionalities.
The different components of the system include:
• On-board APIs for drones with on-board computers
• Off-board APIs for drones without on-board computers
• Separate APIs for raw images, object detection and depth estimation
• Separate APIs for training new image files
• Support for Nvidia-based GTX graphics cards and Cuda 9.0
The institute is keen to establish the following types of partnerships with MNEs or SMEs of all sizes:
i) Licensing agreement where the partner could license the technology and further develop it to introduce it to its customers.
ii) Commercial agreement with technical assistance where the institute would provide support for the Semantic Depth Prediction System.
This SDPS is suitable for, but not limited to, the following applications:
• Logistics/warehouse management
• Law enforcement
• Building/infrastructure inspection
The system offers the following benefits for autonomous UAVs for indoor navigation:
• More cost-effective solution for commercial businesses
• Ensures safer operations in confined indoor spaces as this indoor drone would be smaller in size
• Increases efficiency and productivity
• Reduces human errors and accidents
• No external wall mounted-camera or radio frequency (RF) beacon is required
• Low latency and highly accurate data
• Can be integrated with other sensors
• Able to integrate accurate data into any control system in GPS denied environments such as tunnels and logistic warehouses
• The architecture is easily extendable for future enhancements
The Singapore institute is interested in the following types of partnerships with MNEs or SMEs of all sizes:
i) Licensing agreement - The partner could further develop the technology into a new product/service to be offered to its customers.
ii) Commercial agreement with technical assistance - The partner could utilise the Semantic Depth Prediction System with technical support from the Singapore institute.