GAP9Shield: A 150GOPS AI-capable Ultra-low Power Module for Vision and Ranging Applications on Nano-drones
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Abstract (arXiv)
The evolution of AI and digital signal processing technologies, combined with affordable energy-efficient processors, has propelled the development of both hardware and software for drone applications. Nano-drones, which fit into the palm of the hand, are suitable for indoor environments and safe for human interaction; however, they often fail to deliver the required performance for complex tasks due to the lack of hardware providing sufficient sensing and computing performance. Addressing this gap, we present the GAP9Shield, a nano-drone-compatible module powered by the GAP9, a 150GOPS-capable SoC. The system also includes a 5MP OV5647 camera for high-definition imaging, a WiFi-BLE NINA module, and a 5D VL53L1-based ranging subsystem, which enhances obstacle avoidance capabilities. In comparison with similarly targeted state-of-the-art systems, GAP9Shield provides a 20% higher sample rate (RGB images) while offering a 20% weight reduction. In this paper, we also highlight the energy efficiency and processing power capabilities of GAP9 for object detection (YOLO), localization, and mapping, which can run within a power envelope of below 100 mW and at low latency (as 17 ms for object detection), highlighting the transformative potential of GAP9 for the new generation of nano-drone applications.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 5 of its 7 references that are in the map. First posted on arXiv 27 Jun 2024.
Builds on
5 of 7 references.
References · 5
- 2021Fully Onboard AI-powered Human-Drone Pose Estimation on Ultra-low Power Autonomous Flying Nano-UAVscited by 24
- 2023Ultra-Efficient On-Device Object Detection on AI-Integrated Smart Glasses with TinyissimoYOLOcited by 15
- 2023Bio-inspired Autonomous Exploration Policies with CNN-based Object Detection on Nano-dronescited by 11
- 2023NanoSLAM: Enabling Fully Onboard SLAM for Tiny Robotscited by 5
- 2023Fully Onboard Low-Power Localization with Semantic Sensor Fusion on a Nano-UAV using Floor Planscited by 3
Built on by
Citing papers per year: 2025: 4; 2026: 4.
8 papers.
- 2025An Efficient Ground-aerial Transportation System for Pest Control Enabled by AI-based Autonomous Nano-UAVscited by 4
- 2026Performance Analysis of Edge and In-Sensor AI Processors: A Comparative Reviewcited by 4
- 2025On-Device Federated Continual Learning on RISC-V-based Ultra-Low-Power SoC for Intelligent Nano-Drone Swarmscited by 1
- 2025Efficient and Accurate Downfacing Visual Inertial Odometrycited by 0
- 2025Multi-modal On-Device Learning for Monocular Depth Estimation on Ultra-low-power MCUscited by 0
- 2026Tiny-DroNeRF: Tiny Neural Radiance Fields aboard Federated Learning-enabled Nano-dronescited by 0
- 2026OpenGlass: Ultra-Low-Power On-Device AI Eyewear with Event-based Visioncited by 0
- 2026RISC-V and machine learning: a surveycited by 0
- 2026RISC-V and machine learning: a survey17 Sep 2026
- 2026OpenGlass: Ultra-Low-Power On-Device AI Eyewear with Event-based Vision5 Jun 2026
- 2026Tiny-DroNeRF: Tiny Neural Radiance Fields aboard Federated Learning-enabled Nano-drones2 Mar 2026
- 2026Performance Analysis of Edge and In-Sensor AI Processors: A Comparative Review18 Feb 2026
- 2025Multi-modal On-Device Learning for Monocular Depth Estimation on Ultra-low-power MCUs26 Nov 2025
- 2025Efficient and Accurate Downfacing Visual Inertial Odometry12 Sep 2025
- 2025On-Device Federated Continual Learning on RISC-V-based Ultra-Low-Power SoC for Intelligent Nano-Drone Swarms21 Mar 2025
- 2025An Efficient Ground-aerial Transportation System for Pest Control Enabled by AI-based Autonomous Nano-UAVs20 Feb 2025