Field Robotics Center Seminar
Carnegie Mellon University
Toward Invariant Visual Inertial State Estimation using Information Sparsification
Abstract In this work, we address two current challenges in real-time visual-inertial odometry (VIO) systems - efficiency and accuracy. To this end, we present a novel approach to tightly couple visual and inertial measurements in a fixed-lag VIO framework using information sparsification. To bound computational complexity, fixed-lag smoothers perform marginalization of variables but consequently deteriorate accuracy and [...]
Carnegie Mellon University
Autonomous drone cinematographer: Using artistic principles to create smooth, safe, occlusion-free trajectories for aerial filming
Abstract: Autonomous aerial cinematography has the potential to enable automatic capture of aesthetically pleasing videos without requiring human intervention, empowering individuals with the capability of high-end film studios. Current approaches either only handle off-line trajectory generation, or offer strategies that reason over short time horizons and simplistic representations for obstacles, which result in jerky movement and [...]
Visual SLAM with Semantic Scene understanding
Abstract: Simultaneous localization and mapping (SLAM) has been widely used in autonomous robots and virtual reality. It estimates the sensor motion and maps the environment at the same time. The classic sparse feature point map of visual SLAM is limited for many advanced tasks including robot navigation and interactions, which usually require a high-level understanding of [...]
Carnegie Mellon University
Toward intuitive human controlled MAVs: motion primitives based teleoperation
Abstract: Humans excel at composing high-level plans that achieve a complex, multimodal objective; however, achieving proficiency in teleoperating multi-rotor aerial vehicles (MAVs) in unstructured environments with stability and safety requires significant skill and training. In this talk, we present human-in-the-loop control of a MAV via teleoperation using motion primitives that addresses these concerns. We show [...]
Carnegie Mellon University
Improving Multirotor Trajectory Tracking Performance using Learned Dynamics Models
Abstract: Multirotors and other aerial vehicles have recently seen a surge in popularity, partly due to a rise in industrial applications such as inspection, surveillance, exploration, package delivery, cinematography, and others. Crucial to multirotors' successes in these applications, and enabling their suitability for other applications, is the ability to accurately track trajectories at high speed [...]
Carnegie Mellon University
Automatic Real-time Anomaly Detection for Autonomous Aerial Vehicles
Abstract: The recent incidents with Boeing 737 Max 8 aircraft have raised concerns about the safety and reliability of autopilots and autonomous operations. There is a growing need for methods to monitor the status of aircraft and report any faults and anomalies to the human pilot or to the autopilot to deal with the emergency [...]
Event Cameras: Image Reconstruction, Convolutions and Color
Abstract: Event cameras are novel, bio-inspired visual sensors, whose pixels output asynchronous and independent timestamped spikes at local intensity changes, called ‘events’. Event cameras offer advantages over conventional frame-based cameras in terms of latency, high dynamic range (HDR) and temporal resolution. Event cameras do not output conventional image frames, thus, image reconstruction from events enables [...]
From Farm to Takeoff: Ground and Aerial Robots for Biological Systems Analysis
Abstract: Biological and agricultural environments are dynamic, unstructured, and uncertain, posing challenges for environmental data collection at the necessary spatial and temporal scales to enable meaningful systems analysis. Small unmanned systems, however, can overcome some of these challenges by enabling autonomous or human-assisted image-based and in situ environmental data collection. This talk will present a suite of [...]
AI in Space – From Earth Orbit to Mars and Beyond!
Abstract: Artificial Intelligence is playing an increasing role in our everyday lives and the business marketplace. This trend extends to the space sector, where AI has already shown considerable success and has the potential to revolutionize almost every aspect of space exploration. We first highlight a number of success stories of the tremendous impact of [...]
Carnegie Mellon University
Self-Supervised Learning on Mobile Robots Using Acoustics, Vibration, and Visual Models to Build Rich Semantic Terrain Maps
Abstract: Humans and robots would benefit from having rich semantic maps of the terrain in which they operate. Mobile robots equipped with sensors and perception software could build such maps as they navigate through a new environment. This information could then be used by humans or robots for better localization and path planning, as well [...]