Contextual Segmentation of Fire Spotting Regions Through Satellite-Augmented Autonomous Modular Sensor Imagery
Explore the source record for details and available documents.
Nikhil Swaminathan Behari↗
Engineering topics
Chester Valentine Dolph
Publications and source records attributed to Chester Valentine Dolph.
VSLAM and Coplanar POSIT for Advanced Air Mobility Approach and Landing
Advanced Air Mobility (AAM) aircraft have many challenges for landing accurately and safely in urban, suburban, and rural environments. Localization in large and open rural environments could utilize GPS, but AAM aircraft in urban environments will encounter GPS degradation. Another challenge involves flight operation time, i.e., flying during the day or at night. There are different types of guidelines and landmarks at runways, heliports, and vertiports for daytime and nighttime applications. Tailoring feature detection methods for AAM approach and landing during the day and night pose different issues and challenges. It is easier to detect edges, lines, and other runway markers during the day than at night. Conversely, it is easier to detect landing light configurations and patterns at nighttime than daytime. Consequently, utilizing the same feature detector for daytime and nighttime operations may not be feasible. This paper focuses on vision-based precision approach and landing (PAL) and builds on previous work by comparing ORB SLAM, custom VSLAM, and coplanar pose from orthography and scaling with iterations (POSIT) for daytime and nighttime operations. Implementing high fidelity simulations with computer graphics and extracting video frames from flight test data provide promising results for AAM PAL applications.
Evan Kawamura↗
Distributed Sensing and Computer Vision Methods for Advanced Air Mobility Approach and Landing
Advanced Air Mobility (AAM) aircraft require precision approach and landing systems (PALS) in several types of environments such as urban, suburban, and rural. It is difficult to implement current state-of-the-art methods approved for automated approach and landing for AAM operations. However, existing technology and systems that use vision, IR, radar, and GPS methods provide baseline perception and sensing requirements for AAM aircraft approach and landing. This paper focuses on vision-based PAL and computer vision feature correspondence methods to demonstrate a baseline navigation system while adhering to the Federal Aviation Administration requirements and regulations. The coplanar algorithm determines pose estimation, which feeds into an Extended Kalman filter that combines IMU with vision to create a sensor fusion navigation solution for GPS-denied environments. The state estimate leads to glideslope and localizer error computations, which will be pertinent for designing and deriving guidance laws and control laws for AAM PALS. The IMU and vision navigation solution provides promising simulation results for AAM PALS. This paper builds on previous work by incorporating high fidelity simulations with computer graphics rendering to demonstrate a distributed sensor network to track an AAM aircraft during approach and landing to compare with the aircraft's onboard navigation solution.
Evan Kawamura↗
A Simulation Architecture for Air Traffic Over Urban Environments Supporting Autonomy Research in Advanced Air Mobility
NASA is conducting investigations into Advanced Air Mobility (AAM) concepts, aircraft, and operations. One of the most challenging scenarios for AAM will be enabling safe routine access into densely populated urban centers. To address challenges in the urban environment, a moderately high-fidelity simulation capability is needed to investigate AAM flight operations over a regional area for the development and evaluation of autonomy technologies. This paper describes a system to simulate flight operations around regions such as the San Francisco-Oakland Bay area at a moderately-high scale (10's-100's of flights) that incorporates detailed vehicle models and control necessary to support research in airborne autonomy. The flight vehicle utilizes NASA AAM concept vehicle dynamics integrated with a custom flight management system and flight control system to accurately simulate all phases of flight. Glass cockpit displays have been developed for monitoring aircraft operation over a detailed simulated urban environment. Simulation models have been integrated to simulate air and ground-based sensors, such as radar and LIDAR. The commercial X-Plane software package is used as a rendering engine to mimic vision-based sensors (such as onboard and ground-based cameras) at various times of day and in various weather conditions over a relatively detailed graphical model of the city. The paper presents a detailed illustration of the simulation and software architecture used for traffic over this urban region. This system is enabling the evaluation of NASA research concepts in autonomy for urban AAM operations on the path toward aircraft flight test evaluation.
Keerthana Kannan↗