Nano-U: Efficient Terrain Segmentation for Tiny Robot Navigation

• URL: http://arxiv.org/abs/2605.10210v1
• Date: Mon, 11 May 2026 08:54:49 GMT
• Title: Nano-U: Efficient Terrain Segmentation for Tiny Robot Navigation
• Abstract summary: We develop a complete framework for robust binary terrain segmentation on a low-cost microcontroller.<n>At the core of our approach we design Nano-U, a highly compact binary segmentation network with a few thousand parameters.<n>We train Nano-U via Quantization-Aware Distillation (QAD), combining knowledge distillation and quantization-aware training.<n>We deploy the quantized Nano-U on a commodity microcontroller by extending MicroFlow, a compiler-based inference engine for TinyML implemented in Rust.
• Score: 3.5866729725943576
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Terrain segmentation is a fundamental capability for autonomous mobile robots operating in unstructured outdoor environments. However, state-of-the-art models are incompatible with the memory and compute constraints typical of microcontrollers, limiting scalable deployment in small robotics platforms. To address this gap, we develop a complete framework for robust binary terrain segmentation on a low-cost microcontroller. At the core of our approach we design Nano-U, a highly compact binary segmentation network with a few thousand parameters. To compensate for the network's minimal capacity, we train Nano-U via Quantization-Aware Distillation (QAD), combining knowledge distillation and quantization-aware training. This allows the final quantized model to achieve excellent results on the Botanic Garden dataset and to perform very well on TinyAgri, a custom agricultural field dataset with more challenging scenes. We deploy the quantized Nano-U on a commodity microcontroller by extending MicroFlow, a compiler-based inference engine for TinyML implemented in Rust. By eliminating interpreter overhead and dynamic memory allocation, the quantized model executes on an ESP32-S3 with a minimal memory footprint and low latency. This compiler-based execution demonstrates a viable and energy-efficient solution for perception on low-cost robotic platforms.

Related papers

• NanoCockpit: Performance-optimized Application Framework for AI-based Autonomous Nanorobotics [50.594459728605734]
  Small form factor, i.e., a few 10s grams, severely limits onboard computational resources to sub-SI100milliwatt microcontroller units (MCUs)<n>Our framework achieves ideal end-to-end latency, i.e. zero overhead due to serialized tasks, delivering quantifiable improvements in closed-loop control performance.
  arXiv  Detail & Related papers  (2026-01-12T12:29:38Z)
• Control of Microrobots with Reinforcement Learning under On-Device Compute Constraints [5.456154853270863]
  An important function of autonomous microrobots is the ability to perform robust movement over terrain.<n>This paper explores an edge ML approach to microrobot locomotion, allowing for on-device, lower latency control under compute, memory, and power constraints.
  arXiv  Detail & Related papers  (2025-12-31T09:18:36Z)
• Deeploy: Enabling Energy-Efficient Deployment of Small Language Models On Heterogeneous Microcontrollers [11.365735615086292]
  Deeploy is a novel Deep Neural Network (DNN) compiler, which generates highly-optimized C code requiring minimal runtime support. We demonstrate that Deeploy generates end-to-end code for executing SLMs, fully exploiting the RV32 cores' instruction extensions and the NPU. We achieve leading-edge energy and throughput of SI490microjoule per Token, at SI340Token per second for an SLM trained on the TinyStories dataset.
  arXiv  Detail & Related papers  (2024-08-08T12:40:27Z)
• Training on the Fly: On-device Self-supervised Learning aboard Nano-drones within 20 mW [52.280742520586756]
  Miniaturized cyber-physical systems (CPSes) powered by tiny machine learning (TinyML), such as nano-drones, are becoming an increasingly attractive technology. Simple electronics make these CPSes inexpensive, but strongly limit the computational, memory, and sensing resources available on board. We present a novel on-device fine-tuning approach that relies only on the limited ultra-low power resources available aboard nano-drones.
  arXiv  Detail & Related papers  (2024-08-06T13:11:36Z)
• On-device Self-supervised Learning of Visual Perception Tasks aboard Hardware-limited Nano-quadrotors [53.59319391812798]
  Sub-SI50gram nano-drones are gaining momentum in both academia and industry. Their most compelling applications rely on onboard deep learning models for perception. When deployed in unknown environments, these models often underperform due to domain shift. We propose for the first time, on-device learning aboard nano-drones, where the first part of the in-field mission is dedicated to self-supervised fine-tuning.
  arXiv  Detail & Related papers  (2024-03-06T22:04:14Z)
• High-throughput Visual Nano-drone to Nano-drone Relative Localization using Onboard Fully Convolutional Networks [51.23613834703353]
  Relative drone-to-drone localization is a fundamental building block for any swarm operations. We present a vertically integrated system based on a novel vision-based fully convolutional neural network (FCNN) Our model results in an R-squared improvement from 32 to 47% on the horizontal image coordinate and from 18 to 55% on the vertical image coordinate, on a real-world dataset of 30k images.
  arXiv  Detail & Related papers  (2024-02-21T12:34:31Z)
• MCUFormer: Deploying Vision Transformers on Microcontrollers with Limited Memory [76.02294791513552]
  We propose a hardware-algorithm co-optimizations method called MCUFormer to deploy vision transformers on microcontrollers with extremely limited memory. Experimental results demonstrate that our MCUFormer achieves 73.62% top-1 accuracy on ImageNet for image classification with 320KB memory.
  arXiv  Detail & Related papers  (2023-10-25T18:00:26Z)
• Ultra-low Power Deep Learning-based Monocular Relative Localization Onboard Nano-quadrotors [64.68349896377629]
  This work presents a novel autonomous end-to-end system that addresses the monocular relative localization, through deep neural networks (DNNs), of two peer nano-drones. To cope with the ultra-constrained nano-drone platform, we propose a vertically-integrated framework, including dataset augmentation, quantization, and system optimizations. Experimental results show that our DNN can precisely localize a 10cm-size target nano-drone by employing only low-resolution monochrome images, up to 2m distance.
  arXiv  Detail & Related papers  (2023-03-03T14:14:08Z)
• AutoMoE: Heterogeneous Mixture-of-Experts with Adaptive Computation for Efficient Neural Machine Translation [104.0979785739202]
  Mixture-of-Expert (MoE) models have obtained state-of-the-art performance in Neural Machine Translation (NMT) tasks. Existing MoE models mostly consider a homogeneous design where the same number of experts of the same size are placed uniformly throughout the network. We develop AutoMoE -- a framework for designing heterogeneous MoE's under computational constraints.
  arXiv  Detail & Related papers  (2022-10-14T05:32:17Z)
• Keyword Spotting System and Evaluation of Pruning and Quantization Methods on Low-power Edge Microcontrollers [7.570300579676175]
  Keywords spotting (KWS) is beneficial for voice-based user interactions with low-power devices at the edge. This paper shows our small-footprint KWS system running on STM32F7 microcontroller with Cortex-M7 core @216MHz and 512KB static RAM.
  arXiv  Detail & Related papers  (2022-08-04T16:49:45Z)
• Quantization and Deployment of Deep Neural Networks on Microcontrollers [0.0]
  This work focuses on quantization and deployment of deep neural networks onto low-power 32-bit microcontrollers. A new framework for end-to-end deep neural networks training, quantization and deployment is presented. Execution using single precision 32-bit floating-point as well as fixed-point on 8- and 16-bit integers are supported.
  arXiv  Detail & Related papers  (2021-05-27T17:39:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.