Adaptive and Unsupervised Learning-based 3D Spatio-temporal Filter for Event-driven Cameras

In the evolving landscape of robotics and visual navigation, event cameras have gained significant traction, notably for their exceptional dynamic range, efficient power consumption and low latency. Despite these advantages, conventional processing methods oversimplify the data into two dimensions, neglecting critical temporal information. To overcome this limitation, we propose a novel method that treats events as 3D time-discrete signals. Drawing inspiration from the intricate biological filtering systems inherent to the human visual apparatus, we’ve developed a 3D spatio-temporal filter based on unsupervised machine learning algorithm. This filter effectively reduces noise levels and performs data size reduction, with its parameters being dynamically adjusted based on Population Activity. This ensures adaptability and precision under various conditions, like changes in motion velocity and ambient lighting. In our novel validation approach, we first identify the noise type and determine its power spectral density in the event stream. We then apply a one-dimensional discrete Fast Fourier Transform to assess the filtered event data within the frequency domain, ensuring the targeted noise frequencies are adequately reduced. Our research also delved into the impact of indoor lighting on event stream noise. Remarkably, our method led to a 37% decrease in the data point cloud, improving data quality in diverse outdoor settings

Discussion on event-based cameras for dynamic obstacles recognition and detection for UAVs in outdoor environments

To safely navigate and avoid obstacles in a complex dynamic environment, autonomous drones need a reaction time less than 10 milliseconds. Thus, event-based cameras have increasingly become more widespread in the academic research field for dynamic obstacles detection and avoidance for UAV, as their achievements outperform their frame-based counterparts in term of low-latency. Several publications showed significant results using these sensors. However, most of the experiments relied on indoor data. After a short introduction explaining the differences and features of an event-based camera compared to traditional RGB camera, this work explores the limits of the state-of-art event-based algorithms for obstacles recognition and detection by expanding their results from indoor experiments to real-world outdoor experiments. Indeed, this paper shows the inaccuracy of event-based algorithms for recognition due to insufficient amount of events generated and the inefficiency of event-based obstacles detection algorithms due to the high ration of noise

Exploring the Technical Advances and Limits of Autonomous UAVs for Precise Agriculture in Constrained Environments

In the field of precise agriculture with autonomous unmanned aerial vehicles (UAVs), the utilization of drones holds significant potential to transform crop monitoring, management, and harvesting techniques. However, despite the numerous benefits of UAVs in smart farming, there are still several technical challenges that need to be addressed in order to render their widespread adoption possible, especially in constrained environments. This paper provides a study of the technical aspect and limitations of autonomous UAVs in precise agriculture applications for constrained environments

Autonomous Hybrid Ground/Aerial Mobility in Unknown Environments

Hybrid ground and aerial vehicles can possess distinct advantages over ground-only or flight-only designs in terms of energy savings and increased mobility. In this work we outline our unified framework for controls, planning, and autonomy of hybrid ground/air vehicles. Our contribution is three-fold: 1) We develop a control scheme for the control of passive two-wheeled hybrid ground/aerial vehicles. 2) We present a unified planner for both rolling and flying by leveraging differential flatness mappings. 3) We conduct experiments leveraging mapping and global planning for hybrid mobility in unknown environments, showing that hybrid mobility uses up to five times less energy than flying only