Real-time indoor autonomous vehicle test environment

By Jonathan P. How, Brett Bethke, Adrian A. Frank, D. Dale, John L. Vian. 2008

467 citations recorded by the source corpus.

Subfields: Automotive Engineering, Computer Networks and Communications, Computer Vision and Pattern Recognition

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Citations in the corpus

BADGR: An Autonomous Self-Supervised Learning-Based Navigation System (2020, background): “Autonomous mobile robot navigation has been extensively studied in many real-world scenarios, ranging from indoor navigation [2], [6], [7] to outdoor driving [3], [8].”
Adaptive Control of Quadrotor UAVs: A Design Trade Study With Flight Evaluations (2013, background): “been applied to a variety of problems, including visionbased sensing algorithms for indoor flight [7]–[9], dynamic”
Quadrotors and Accelerometers: State Estimation with an Improved Dynamic Model (2014, methodology): “The widely used traditional quadrotor model (for example, [4]–[11]) assumes that the significant forces acting on the vehicle are gravity and the thrust produced by the rotors (see Figure 1).”
A simple learning strategy for high-speed quadrocopter multi-flips (2010, methodology): “Most research has so far been focused on near-hover mode operation using simpliﬁed linear models, with a variety of extensions such as autonomous long-term operation [1] and various controller design methodologies such as in [3], [4].”
A Survey on Aerial Swarm Robotics (2018, background): “Other indoor swarms utilize motion capture systems for localization [207], [208].”
A survey on vision-based UAV navigation (2018, background): “2009), University of Pennsylvania (How et al. 2008) and many other top universities are also vigorously developing research on vision-based UAV navigation and have incorporated this technology into next generation air transport systems, such as NextGen (Strohmeier et al.”
Monocular vision SLAM for indoor aerial vehicles (2009, unknown): “In contrast with other prior works that predominantly used wireless video feeds and Vicon vision tracking system for vehicle state estimation [24], SaintVertigo performs all image processing and SLAM computations onboard, with a 1 GHz CPU, 1 GB RAM, and 4GB storage.”
Polynomial Trajectory Planning for Aggressive Quadrotor Flight in Dense Indoor Environments (2016, background): “Recent advances in small unmanned aircraft have enabled highly dynamic, aerobatic flight maneuvers [1, 9, 10, 21].”
Stereo vision-based obstacle avoidance for micro air vehicles using disparity space (2014, methodology): “The planner uses the model described in [19] and designs a series of waypoint”
A survey of quadrotor Unmanned Aerial Vehicles (2012, methodology): “Real-time Indoor Autonomous Vehicle Test environment or RAVEN is another testbed designed to explore long duration autonomous air operation using multiple UAVs [39, 65].”
Survey of advances in guidance, navigation, and control of unmanned rotorcraft systems (2012, methodology): “Another PRM-based path planner for a robotic helicopter was presented in Hrabar (2008). The road map is constructed based on an occupancy grid representation, and a D Lite algorithm is used to search for the shortest collision-free path.”
Autonomous flight in unstructured and unknown indoor environments (2009, background): “While some authors [5, 6] have demonstrated indoor flight using GPS simulated from motion capture systems, we seek to develop flying robots that are able to operate autonomously while carrying all sensors used for localizatio n, control and navigation onboard.”