# Navion: A 2mW Fully Integrated Real-Time Visual-Inertial Odometry Accelerator for Autonomous Navigation of Nano Drones

arXiv: https://arxiv.org/abs/1809.05780 · HTML: https://mlsanity.com/paper/1809.05780/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Sep 2018.

## Built on by (most cited first, 15 of 15)

- [NanoFlowNet: Real-time Dense Optical Flow on a Nano Quadcopter](https://mlsanity.com/paper/2209.06918/) (2022)
- [ColibriUAV: An Ultra-Fast, Energy-Efficient Neuromorphic Edge Processing UAV-Platform with Event-Based and Frame-Based Cameras](https://mlsanity.com/paper/2305.18371/) (2023)
- [EfficientNav: Towards On-Device Object-Goal Navigation with Navigation Map Caching and Retrieval](https://mlsanity.com/paper/2510.18546/) (2025)
- [Low Latency Visual Inertial Odometry with On-Sensor Accelerated Optical Flow for Resource-Constrained UAVs](https://mlsanity.com/paper/2406.13345/) (2024)
- [A Heterogeneous RISC-V based SoC for Secure Nano-UAV Navigation](https://mlsanity.com/paper/2401.03531/) (2024)
- [An Energy-Efficient Quad-Camera Visual System for Autonomous Machines on FPGA Platform](https://mlsanity.com/paper/2104.00192/) (2021)
- [Is That Rain? Understanding Effects on Visual Odometry Performance for Autonomous UAVs and Efficient DNN-based Rain Classification at the Edge](https://mlsanity.com/paper/2407.12663/) (2024)
- [LEVIO: Lightweight Embedded Visual Inertial Odometry for Resource-Constrained Devices](https://mlsanity.com/paper/2602.03294/) (2026)
- [A Motion-Aware Vector Quantization Framework with Centroid Reuse for Efficient VLA Inference](https://mlsanity.com/paper/2607.24148/) (2026)
- [AutoPilot: Automating SoC Design Space Exploration for SWaP Constrained Autonomous UAVs](https://mlsanity.com/paper/2102.02988/) (2021)
- [Training Lightweight CNNs for Human-Nanodrone Proximity Interaction from Small Datasets using Background Randomization](https://mlsanity.com/paper/2110.14491/) (2021)
- [Urban Drone Navigation: Autoencoder Learning Fusion for Aerodynamics](https://mlsanity.com/paper/2310.08830/) (2023)
- [On the Importance of Neural Membrane Potential Leakage for LIDAR-based Robot Obstacle Avoidance using Spiking Neural Networks](https://mlsanity.com/paper/2507.09538/) (2025)
- [Efficient and Accurate Downfacing Visual Inertial Odometry](https://mlsanity.com/paper/2509.10021/) (2025)
- [HexVIO: Towards All-Day Stereo-Inertial Tracking Through Commodity DSPs](https://mlsanity.com/paper/2610.03283/) (2026)

## Abstract (arXiv)

This paper presents Navion, an energy-efficient accelerator for visual-inertial odometry (VIO) that enables autonomous navigation of miniaturized robots (e.g., nano drones), and virtual/augmented reality on portable devices. The chip uses inertial measurements and mono/stereo images to estimate the drone's trajectory and a 3D map of the environment. This estimate is obtained by running a state-of-the-art VIO algorithm based on non-linear factor graph optimization, which requires large irregularly structured memories and heterogeneous computation flow. To reduce the energy consumption and footprint, the entire VIO system is fully integrated on chip to eliminate costly off-chip processing and storage. This work uses compression and exploits both structured and unstructured sparsity to reduce on-chip memory size by 4.1×. Parallelism is used under tight area constraints to increase throughput by 43%. The chip is fabricated in 65nm CMOS, and can process 752×480 stereo images from EuRoC dataset in real-time at 20 frames per second (fps) consuming only an average power of 2mW. At its peak performance, Navion can process stereo images at up to 171 fps and inertial measurements at up to 52 kHz, while consuming an average of 24mW. The chip is configurable to maximize accuracy, throughput and energy-efficiency trade-offs and to adapt to different environments. To the best of our knowledge, this is the first fully integrated VIO system in an ASIC.