Microelectronic 3D Imaging and Neuromorphic Recognition for Autonomous UAVs

Zappa, Franco; Villa, Federica; Lussana, Rudi; Delic, Dennis; Mau, Man Ching Joyce; Redouté, Jean Michel; Kennedy, Scott; Morrison, Daniel; Yuce, Mehmet Rasit; Alan, Tuncay; Hamilton, Tara Julia; Afshar, Saeed

doi:10.1007/978-94-024-2021-0_17

Cited by 2 publications

(1 citation statement)

References 9 publications

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“…In Cohen et al (2019) and Afshar et al (2020b) , the event-based time surface representations for event-based vision data have been used in extracting features for a range of tasks, such as object recognition on unmanned aerial vehicles (UAVs) ( Zappa et al, 2020 ) and single photon avalanche diode (SPAD) sensors data processing ( Afshar et al, 2020a ).…”

Section: Introductionmentioning

confidence: 99%

Event-driven spectrotemporal feature extraction and classification using a silicon cochlea model

Perera

Bethi

et al. 2023

Front. Neurosci.

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This paper presents a reconfigurable digital implementation of an event-based binaural cochlear system on a Field Programmable Gate Array (FPGA). It consists of a pair of the Cascade of Asymmetric Resonators with Fast Acting Compression (CAR-FAC) cochlea models and leaky integrate-and-fire (LIF) neurons. Additionally, we propose an event-driven SpectroTemporal Receptive Field (STRF) Feature Extraction using Adaptive Selection Thresholds (FEAST). It is tested on the TIDIGTIS benchmark and compared with current event-based auditory signal processing approaches and neural networks.

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Section: Introductionmentioning

confidence: 99%

Event-driven spectrotemporal feature extraction and classification using a silicon cochlea model

Perera

Bethi

et al. 2023

Front. Neurosci.

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Towards a Multi-Pixel Time-of-Flight Indoor Navigation System for Nano-Drone Applications

Niculescu

Muller

Ostovar

et al. 2022

2022 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)

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Perceiving and mapping the surroundings are essential for enabling autonomous navigation in any robotic platform. The algorithm class that enables accurate mapping while correcting the odometry errors present in most robotics systems is Simultaneous Localization and Mapping (SLAM). Today, fully onboard mapping is only achievable on robotic platforms that can host high-wattage processors, mainly due to the significant computational load and memory demands required for executing SLAM algorithms. For this reason, pocketsize hardware-constrained robots offload the execution of SLAM to external infrastructures. To address the challenge of enabling SLAM algorithms on resource-constrained processors, this paper proposes NanoSLAM, a lightweight and optimized end-to-end SLAM approach specifically designed to operate on centimetersize robots at a power budget of only 87.9 mW. We demonstrate the mapping capabilities in real-world scenarios and deploy NanoSLAM on a nano-drone weighing 44 g and equipped with a novel commercial RISC-V low-power parallel processor called GAP9. The algorithm is designed to leverage the parallel capabilities of the RISC-V processing cores and enables mapping of a general environment with an accuracy of 4.5 cm and an end-to-end execution time of less than 250 ms.

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Microelectronic 3D Imaging and Neuromorphic Recognition for Autonomous UAVs

Cited by 2 publications

References 9 publications

Event-driven spectrotemporal feature extraction and classification using a silicon cochlea model

Event-driven spectrotemporal feature extraction and classification using a silicon cochlea model

Towards a Multi-Pixel Time-of-Flight Indoor Navigation System for Nano-Drone Applications

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