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BatDeck - Ultra Low-power Ultrasonic Ego-velocity Estimation and Obstacle Avoidance on Nano-drones

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arXiv 2024年

作者： Müller, Hanna Kartsch, Victor Magno, Michele Benini, Luca Integrated Systems Laboratory Center for Project-Based Learning ETH Zürich Switzerland DEI University of Bologna Italy

Nano-drones, with their small, lightweight design, are ideal for confined-space rescue missions and inherently safe for human interaction. However, their limited payload restricts the critical sensing needed for ego-velocity estimation and obstacle detection to single-bean laser-based time-of-flight (ToF) and low-resolution optical sensors. Although those sensors have demonstrated good performance, they fail in some complex real-world scenarios, especially when facing transparent or reflective surfaces (ToFs) or when lacking visual features (optical-flow sensors). Taking inspiration from bats, this paper proposes a novel two-way ranging-based method for ego-velocity estimation and obstacle avoidance based on down-and-forward facing ultra-low-power ultrasonic sensors, which improve the performance when the drone faces reflective materials or navigates in complete darkness. Our results demonstrate that our new sensing system achieves a mean square error of 0.019 m/s on ego-velocity estimation and allows exploration for a flight time of 8 minutes while covering 136 m on average in a challenging environment with transparent and reflective obstacles. We also compare ultrasonic and laser-based ToF sensing techniques for obstacle avoidance, as well as optical flow and ultrasonic-based techniques for ego-velocity estimation, denoting how these systems and methods can be complemented to enhance the robustness of nano-drone operations. © 2024, CC BY.

关键词： Drones

BatDeck: Advancing Nano-Drone Navigation with Low-Power Ultrasound-based Obstacle Avoidance

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BatDeck: Advancing Nano-Drone Navigation with Low-Power Ultr...

IEEE Sensors Applications Symposium, SAS

作者： Hanna Müller Victor Kartsch Michele Magno Luca Benini Integrated Systems Laboratory Center for Project-Based Learning – ETH Zürich Switzerland DEI - University of Bologna Italy

ISBN: (数字)9798350369250

ISBN: (纸本)9798350369267

Nano-drones, distinguished by their agility, minimal weight, and cost-effectiveness, are particularly well-suited for exploration in confined, cluttered and narrow spaces. Recognizing transparent, highly reflective or absorbing materials, such as glass and metallic surfaces is challenging, as classical sensors such as cameras or laser rangers often do not detect them. Inspired by bats, which can fly at high speeds in complete darkness with the help of ultrasound, this paper introduces BatDeck, a pioneering sensor-deck employing a lightweight and low-power ultrasonic sensor for nano-drone autonomous navigation. This paper first provides insights about sensor characteristics, highlighting the influence of motor noise on the ultrasound readings, then it introduces the results of extensive experimental tests for obstacle avoidance (OA) in a diverse environment. Results show that BatDeck allows exploration for a flight time of 8 minutes while covering 136m on average before crash in a challenging environment with transparent and reflective obstacles, proving the effectiveness of ultrasonic sensors for OA on nano-drones.

关键词： Ultrasonic imaging Navigation Noise Sensor phenomena and characterization Sensor fusion Motors Sensor systems and applications

BatDeck: Advancing Nano-drone Navigation with Low-power Ultrasound-based Obstacle Avoidance

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arXiv 2024年

作者： Müller, Hanna Kartsch, Victor Magno, Michele Benini, Luca Integrated Systems Laboratory Center for Project-Based Learning - ETH Zürich Switzerland DEI - University of Bologna Italy

关键词： Cost effectiveness

Skilog: A Smart Sensor System for Performance Analysis and Biofeedback in Ski Jumping

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Skilog: A Smart Sensor System for Performance Analysis and B...

2023 IEEE Biomedical Circuits and Systems Conference, BioCAS 2023

作者： Schulthess, Lukas Ingolfsson, Thorir Mar Nolke, Marc Magno, Michele Benini, Luca Leitner, Christoph Eth Zürich Center for Project Based Learning Zürich Switzerland Pzn Krakow Poland Eth Zürich Integrated Systems Laboratory Zürich Switzerland University of Bologna Dei Bologna Italy

ISBN: (纸本)9798350300260

In ski jumping, low repetition rates of jumps limit the effectiveness of training. Thus, increasing learning rate within every single jump is key to success. A critical element of athlete training is motor learning, which has been shown to be accelerated by feedback methods. In particular, a fine-grained control of the center of gravity in the in-run is essential. This is because the actual takeoff occurs within a blink of an eye (∼ 300 ms), thus any unbalanced body posture during the in-run will affect *** paper presents a smart, compact, and energy-efficient wireless sensor system for real-time performance analysis and biofeedback during ski jumping. The system operates by gauging foot pressures at three distinct points on the insoles of the ski boot at 100 Hz. Foot pressure data can either be directly sent to coaches to improve their feedback, or fed into a Machine learning (ML) model to give athletes instantaneous in-action feedback using a vibration motor in the ski boot. In the biofeedback scenario, foot pressures act as input variables for an optimized XGBoost model. We achieve a high predictive accuracy of 92.7 % for center of mass predictions (dorsal shift, neutral stand, ventral shift). Subsequently, we parallelized and fine-tuned our XGBoost model for a RISC-V based low power parallel processor (GAP9), based on the Parallel Ultra-Low Power (PULP) architecture. We demonstrate real-time detection and feedback (0.0109 ms/inference) using our on-chip deployment. The proposed smart system is unobtrusive with a slim form factor (13 mm baseboard, 3.2 mm antenna) and a lightweight build (26 g). Power consumption analysis reveals that the system's energy-efficient design enables sustained operation over multiple days (up to 300 hours) without requiring recharge. © 2023 IEEE.

关键词： Biomechanics Data logger Sensor Sport tinyML Wearable Wireless

Low Latency Visual Inertial Odometry with On-Sensor Accelerated Optical Flow for Resource-Constrained UAVs

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arXiv 2024年

作者： Kühne, Jonas Magno, Michele Benini, Luca The Integrated Systems Laboratory The Center for Project-Based Learning ETH Zurich Zurich8092 Switzerland The Center for Project-Based Learning ETH Zurich Zurich8092 Switzerland The Integrated Systems Laboratory ETH Zurich Zurich8092 Switzerland The Department of Electrical Electronic and Information Engineering University of Bologna Bologna40136 Italy

Visual Inertial Odometry (VIO) is the task of estimating the movement trajectory of an agent from an onboard camera stream fused with additional Inertial Measurement Unit (IMU) measurements. A crucial subtask within VIO is the tracking of features, which can be achieved through Optical Flow (OF). As the calculation of OF is a resource-demanding task in terms of computational load and memory footprint, which needs to be executed at low latency, especially in robotic applications, OF estimation is today performed on powerful CPUs or GPUs. This restricts its use in a broad spectrum of applications where the deployment of such powerful, power-hungry processors is unfeasible due to constraints related to cost, size, and power consumption. On-sensor hardware acceleration is a promising approach to enable low latency VIO even on resource-constrained devices such as nano drones. This paper assesses the speed-up in a VIO sensor system exploiting a compact OF sensor consisting of a global shutter camera and an Application Specific Integrated Circuit (ASIC). By replacing the feature tracking logic of the VINS-Mono pipeline with data from this OF camera, we demonstrate a 49.4% reduction in latency and a 53.7% reduction of compute load of the VIO pipeline over the original VINS-Mono implementation, allowing VINS-Mono operation up to 50 FPS instead of 20 FPS on the quad-core ARM Cortex-A72 processor of a Raspberry Pi Compute Module 4. Copyright © 2024, The Authors. All rights reserved.

关键词： Optical flows

GP-enhanced Autonomous Drifting Framework using ADMM-based iLQR

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arXiv 2025年

作者： Xie, Yangyang Hu, Cheng Baumann, Nicolas Ghignone, Edoardo Magno, Michele Xie, Lei State Key Laboratory of Industrial Control Technology Zhejiang University Zhejiang Hangzhou310027 China Center for Project-Based Learning D-ITET ETH Zurich Switzerland

Autonomous drifting is a complex challenge due to the highly nonlinear dynamics and the need for precise real-time control, especially in uncertain environments. To address these limitations, this paper presents a hierarchical control framework for autonomous vehicles drifting along general paths, primarily focusing on addressing model inaccuracies and mitigating computational challenges in real-time control. The framework integrates Gaussian Process (GP) regression with an Alternating Direction Method of Multipliers (ADMM)-based iterative Linear Quadratic Regulator (iLQR). GP regression effectively compensates for model residuals, improving accuracy in dynamic conditions. ADMM-based iLQR not only combines the rapid trajectory optimization of iLQR but also utilizes ADMM’s strength in decomposing the problem into simpler sub-problems. Simulation results demonstrate the effectiveness of the proposed framework, with significant improvements in both drift trajectory tracking and computational efficiency. Our approach resulted in a 38% reduction in RMSE lateral error and achieved an average computation time that is 75% lower than that of the Interior Point OPTimizer (IPOPT). © 2025, CC BY.

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Fully On-board Low-Power Localization with Multizone Time-of-Flight Sensors on Nano-UAVs

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Fully On-board Low-Power Localization with Multizone Time-of...

Design, Automation and Test in Europe Conference and Exhibition

作者： Hanna Müller Nicky Zimmerman Tommaso Polonelli Michele Magno Jens Behley Cyrill Stachniss Luca Benini Integrated Systems Laboratory Center for Project-Based Learning - ETH Zürich Switzerland Institute of Geodesy and Geoinformation - University of Bonn

Nano-size unmanned aerial vehicles (UAVs) hold enormous potential to perform autonomous operations in complex environments, such as inspection, monitoring or data collection. Moreover, their small size allows safe operation close to humans and agile flight. An important part of autonomous flight is localization, a computationally intensive task, especially on a nano-UAV that usually has strong constraints in sensing, processing and memory. This work presents a real-time localization approach with low-element-count multizone range sensors for resource-constrained nano-UAVs. The proposed approach is based on a novel miniature 64-zone time-of-flight sensor from STMicroelectronics and a RISC-V-based parallel ultra-low-power processor to enable accurate and low latency Monte Carlo Localization on-board. Experimental evaluation using a nano-UAV open platform demonstrated that the proposed solution is capable of localizing on a 31.2 m 2 map with 0.15 m accuracy and an above 95% success rate. The achieved accuracy is sufficient for localization in common indoor environments. We analyze trade-offs in using full and half-precision floating point numbers as well as a quantized map and evaluate the accuracy and memory footprint across the design space. Experimental evaluation shows that parallelizing the execution for 8 RISC-V cores brings a 7x speedup and allows us to execute the algorithm onboard in real-time with a latency of 0.2-30 ms (depending on the number of particles), while only increasing the overall drone power consumption by 3–7%. Finally, we provide an open-source implementation of our approach.

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Towards a Novel Ultrasound System based on Low-Frequency Feature Extraction From a Fully-Printed Flexible Transducer

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Towards a Novel Ultrasound System Based on Low-Frequency Fea...

2023 IEEE Biomedical Circuits and Systems Conference, BioCAS 2023

作者： Giordano, Marco Keller, Kirill Greco, Francesco Benini, Luca Magno, Michele Leitner, Christoph Eth Zürich Center for Project Based Learning Integrated Systems Laboratory Zürich Switzerland Graz University of Technology Institute of Solid State Physics Graz Austria Sant'Anna School of Advanced Studies BioRobotics Institute Pisa Italy University of Bologna Dei Bologna Italy

ISBN: (纸本)9798350300260

Ultrasound is a key technology in healthcare, and it is being explored for non-invasive, wearable, continuous monitoring of vital signs. However, its widespread adoption in this scenario is still hindered by the size, complexity, and power consumption of current devices. Moreover, such an application demands adaptability to human anatomy, which is hard to achieve with current transducer technology. This paper presents a novel ultrasound system prototype based on a fully printed, lead-free, and flexible polymer ultrasound transducer, whose bending radius promises good adaptability to the human anatomy. Our application scenario focuses on continuous blood flow monitoring. We implemented a hardware envelope filter to efficiently transpose high-frequency ultrasound signals to a lower-frequency spectrum. This reduces computational and power demands with little to no degradation in the task proposed for this work. We validated our method on a setup that mimics human blood flow by using a flow phantom and a peristaltic pump simulating 3 different heartbeat rhythms: 60, 90 and 120 beats per minute. Our ultrasound setup reconstructs peristaltic pump frequencies with errors of less than 0.05 Hz (3 bpm) from the set pump frequency, both for the raw echo and the enveloped echo. The analog pre-processing showed a promising reduction of signal bandwidth of more than 6x: pulse-echo signals of transducers excited at 12.5 MHz were reduced to about 2 MHz. Thus, allowing consumer MCUs to acquire and elaborate signals within mW-power range in an inexpensive fashion. © 2023 IEEE.

关键词： Cardiovascular response Medical P(VDF-TrFE) Ultrasonics

ODDN: Addressing Unpaired Data Challenges in Open-World Deepfake Detection on Online Social Networks 39

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ODDN: Addressing Unpaired Data Challenges in Open-World Deep...

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Tao, Renshuai Le, Manyi Tan, Chuangchuang Liu, Huan Qin, Haotong Zhao, Yao Institute of Information Science Beijing Jiaotong University China Visual Intellgence +X International Cooperation Joint Laboratory of MOE China Center for Project-Based Learning (PBL) D-ITET Switzerland

ISBN: (纸本)157735897X

Despite significant advances in deepfake detection, handling varying image quality, especially due to different compressions on online social networks (OSNs), remains challenging. Current methods succeed by leveraging correlations between paired images, whether raw or compressed. However, in open-world scenarios, paired data is scarce, with compressed images readily available but corresponding raw versions difficult to obtain. This imbalance, where unpaired data vastly outnumbers paired data, often leads to reduced detection performance, as existing methods struggle without corresponding raw images. To overcome this issue, we propose a novel approach named the open-world deepfake detection network (ODDN), which comprises two core modules: open-world data aggregation (ODA) and compression-discard gradient correction (CGC). ODA effectively aggregates correlations between compressed and raw samples through both fine-grained and coarse-grained analyses for paired and unpaired data, respectively. CGC incorporates a compression-discard gradient correction to further enhance performance across diverse compression methods in OSN. This technique optimizes the training gradient to ensure the model remains insensitive to compression variations. Extensive experiments conducted on 17 popular deepfake datasets demonstrate the superiority of the ODDN over SOTA baselines. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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