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1. Learning sensorimotor control with neuromorphic sensors: Toward hyperdimensional active perception

   https://doi.org/10.1126/scirobotics.aaw6736  

   Mitrokhin, A.; Sutor, P.; Fermüller, C.; Aloimonos, Y. (May 2019, Science Robotics)

   The hallmark of modern robotics is the ability to directly fuse the platform’s perception with its motoric ability—the concept often referred to as “active perception.” Nevertheless, we find that action and perception are often kept in separated spaces, which is a consequence of traditional vision being frame based and only existing in the moment and motion being a continuous entity. This bridge is crossed by the dynamic vision sensor (DVS), a neuromorphic camera that can see the motion. We propose a method of encoding actions and perceptions together into a single space that is meaningful, semantically informed, and consistent by using hyperdimensional binary vectors (HBVs). We used DVS for visual perception and showed that the visual component can be bound with the system velocity to enable dynamic world perception, which creates an opportunity for real-time navigation and obstacle avoidance. Actions performed by an agent are directly bound to the perceptions experienced to form its own “memory.” Furthermore, because HBVs can encode entire histories of actions and perceptions—from atomic to arbitrary sequences—as constant-sized vectors, autoassociative memory was combined with deep learning paradigms for controls. We demonstrate these properties on a quadcopter drone ego-motion inference task and the MVSEC (multivehicle stereo event camera) dataset. 

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2. EV-IMO: Motion Segmentation Dataset and Learning Pipeline for Event Cameras

   https://doi.org/10.1109/IROS40897.2019.8968520  

   Mitrokhin, Anton; Ye, Chengxi Ye; Fermüller, Cornelia; Aloimonos, Yiannis; Delbruck, Tobi (November 2019, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS))

   We present the first event-based learning approach for motion segmentation in indoor scenes and the first event-based dataset – EV-IMO – which includes accurate pixel-wise motion masks, egomotion and ground truth depth. Our approach is based on an efficient implementation of the SfM learning pipeline using a low parameter neural network architecture on event data. In addition to camera egomotion and a dense depth map, the network estimates independently moving object segmentation at the pixel-level and computes per-object 3D translational velocities of moving objects. We also train a shallow network with just 40k parameters, which is able to compute depth and egomotion. Our EV-IMO dataset features 32 minutes of indoor recording with up to 3 fast moving objects in the camera field of view. The objects and the camera are tracked using a VICON motion capture system. By 3D scanning the room and the objects, ground truth of the depth map and pixel-wise object masks are obtained. We then train and evaluate our learning pipeline on EV-IMO and demonstrate that it is well suited for scene constrained robotics applications. 

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3. Retina-inspired Object Motion Segmentation for Event-Cameras

   https://doi.org/10.1109/NICE65350.2025.11065149  

   Clerico, Victoria; Snyder, Shay; Lohia, Arya; Kaiser, Md Abdullah-Al; Schwartz, Gregory; Jaiswal, Akhilesh; Parsa, Maryam (March 2025, IEEE)

   Event-cameras have emerged as a revolutionary technology with a high temporal resolution that far surpasses standard active pixel cameras. This technology draws biological inspiration from photoreceptors and the initial retinal synapse. This research showcases the potential of additional retinal functionalities to extract visual features. We provide a domain-agnostic and efficient algorithm for ego-motion compensation based on Object Motion Sensitivity (OMS), one of the multiple features computed within the mammalian retina. We develop a method based on experimental neuroscience that translates OMS’ biological circuitry to a low-overhead algorithm to suppress camera motion bypassing the need for deep networks and learning. Our system processes event data from dynamic scenes to perform pixel-wise object motion segmentation using a real and synthetic dataset. This paper introduces a bio-inspired computer vision method that dramatically reduces the number of parameters by 10^3 to 10^6 orders of magnitude compared to previous approaches. Our work paves the way for robust, high-speed, and low-bandwidth decision-making for in-sensor computations. 

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4. Bio-mimetic high-speed target localization with fused frame and event vision for edge application

   https://doi.org/10.3389/fnins.2022.1010302  

   Lele, Ashwin Sanjay; Fang, Yan; Anwar, Aqeel; Raychowdhury, Arijit (November 2022, Frontiers in Neuroscience)

   Evolution has honed predatory skills in the natural world where localizing and intercepting fast-moving prey is required. The current generation of robotic systems mimics these biological systems using deep learning. High-speed processing of the camera frames using convolutional neural networks (CNN) (frame pipeline) on such constrained aerial edge-robots gets resource-limited. Adding more compute resources also eventually limits the throughput at the frame rate of the camera as frame-only traditional systems fail to capture the detailed temporal dynamics of the environment. Bio-inspired event cameras and spiking neural networks (SNN) provide an asynchronous sensor-processor pair (event pipeline) capturing the continuous temporal details of the scene for high-speed but lag in terms of accuracy. In this work, we propose a target localization system combining event-camera and SNN-based high-speed target estimation and frame-based camera and CNN-driven reliable object detection by fusing complementary spatio-temporal prowess of event and frame pipelines. One of our main contributions involves the design of an SNN filter that borrows from the neural mechanism for ego-motion cancelation in houseflies. It fuses the vestibular sensors with the vision to cancel the activity corresponding to the predator's self-motion. We also integrate the neuro-inspired multi-pipeline processing with task-optimized multi-neuronal pathway structure in primates and insects. The system is validated to outperform CNN-only processing using prey-predator drone simulations in realistic 3D virtual environments. The system is then demonstrated in a real-world multi-drone set-up with emulated event data. Subsequently, we use recorded actual sensory data from multi-camera and inertial measurement unit (IMU) assembly to show desired working while tolerating the realistic noise in vision and IMU sensors. We analyze the design space to identify optimal parameters for spiking neurons, CNN models, and for checking their effect on the performance metrics of the fused system. Finally, we map the throughput controlling SNN and fusion network on edge-compatible Zynq-7000 FPGA to show a potential 264 outputs per second even at constrained resource availability. This work may open new research directions by coupling multiple sensing and processing modalities inspired by discoveries in neuroscience to break fundamental trade-offs in frame-based computer vision 1 . 

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5. Learning Visual Motion Segmentation using Event Surfaces

   Mitrokhin, A. (January 2020, IEEE Conference on Computer Vision and Pattern Recognition)

   Event-based cameras have been designed for scene motion perception - their high temporal resolution and spatial data sparsity converts the scene into a volume of boundary trajectories and allows to track and analyze the evolution of the scene in time. Analyzing this data is computationally expensive, and there is substantial lack of theory on dense-in-time object motion to guide the development of new algorithms; hence, many works resort to a simple solution of discretizing the event stream and converting it to classical pixel maps, which allows for application of conventional image processing methods. In this work we present a Graph Convolutional neural network for the task of scene motion segmentation by a moving camera. We convert the event stream into a 3D graph in (x,y,t) space and keep per-event temporal information. The difficulty of the task stems from the fact that unlike in metric space, the shape of an object in (x,y,t) space depends on its motion and is not the same across the dataset. We discuss properties of of the event data with respect to this 3D recognition problem, and show that our Graph Convolutional architecture is superior to PointNet++. We evaluate our method on the state of the art event-based motion segmentation dataset - EV-IMO and perform comparisons to a frame-based method proposed by its authors. Our ablation studies show that increasing the event slice width improves the accuracy, and how subsampling and edge configurations affect the network performance. 

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