This paper proposes an AR-based real-time mobile system for assistive indoor navigation with target segmentation (ARMSAINTS) for both sighted and blind or low-vision (BLV) users to safely explore and navigate in an indoor environment. The solution comprises four major components: graph construction, hybrid modeling, real-time navigation and target segmentation. The system utilizes an automatic graph construction method to generate a graph from a 2D floorplan and the Delaunay triangulation-based localization method to provide precise localization with negligible error. The 3D obstacle detection method integrates the existing capability of AR with a 2D object detector and a semantic target segmentation model to detect and track 3D bounding boxes of obstacles and people to increase BLV safety and understanding when traveling in the indoor environment. The entire system does not require the installation and maintenance of expensive infrastructure, run in real-time on a smartphone, and can easily adapt to environmental changes.
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Autonomous Scooter Navigation for People with Mobility Challenges
Despite the technical success of existing assistive technologies, for example, electric wheelchairs and scooters, they are still far from effective enough in helping the blind and elderly navigate to their destinations in a hassle-free manner. Riders often face challenges in driving scooters in some indoor and crowded places, especially on sidewalks with numerous obstacles and other pedestrians. People with certain disabilities, such as the blind, are often unable to drive their scooters well enough. In this paper, we propose to improve the safety and autonomy of the navigation by designing a cutting-edge autonomous scooter, which allows people with mobility challenges to navigate independently and safely in possibly unfamiliar surroundings. We focus on the localization and navigation challenges for the autonomous scooter where the current location, maps, and nearby obstacles are unknown. Solving these challenges will enable the scooter to both travel within buildings and perform tight maneuvers in densely crowds automatically.
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- Award ID(s):
- 1637371
- NSF-PAR ID:
- 10092487
- Date Published:
- Journal Name:
- 2018 IEEE International Conference on Cognitive Computing (ICCC)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ICCC.2018.00020
