Flying Fast and Low Among Obstacles: Methodology and Experiments

By Sebastian A. Scherer, Sanjiv Singh, Lyle Chamberlain, M. Elgersma. 2008

202 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Automotive Engineering, 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, 10, 11, 12] to outdoor driving [3, 13, 14, 15, 16].”
Learning to fly by crashing (2017, background): “Most early research focused on a two-step approach: the first step being perception where underlying occupancy/obstacle map is estimated; the second step is to use the predicted depth map to issue motor commands to travel in freespace [1],”
PIXHAWK: A micro aerial vehicle design for autonomous flight using onboard computer vision (2012, background): “Impressive results have been shown in terms of autonomous take-off, landing and navigation as well as obstacle avoidance (Conte and Doherty 2008; Saripalli et al. 2002; Johnson et al. 2005; Bosch et al. 2006; Scherer et al. 2008; Proctor et al. 2006; Kanade et al. 2004; Hrabar and Sukhatme 2009).”
A Fully Autonomous Indoor Quadrotor (2012, methodology): “[28] describe algorithms for flying fast among obstacles at low altitude using a laser scanner.”
Toward low-flying autonomous MAV trail navigation using deep neural networks for environmental awareness (2017, background): “Some of the challenges of ﬂying low among obstacles are described by Scherer et al. [30].”
Unmanned aerial systems for photogrammetry and remote sensing: A review (2014, background): “In these cases, obstacle detection and avoidance (Scherer et al., 2008) or short-range mapping have been reported.”
State estimation for aggressive flight in GPS-denied environments using onboard sensing (2012, background): “Scherer et al. use laser rangeﬁnders to build occupancy maps, and avoid obstacles while ﬂying fast through obstacles [17], however they rely on accurate GPS measurements for state estimation, and do not focus on state estimation.”
Survey of advances in guidance, navigation, and control of unmanned rotorcraft systems (2012, methodology): “Therefore, among the papers surveyed, most of the practical implementation (Bachrach et al., 2009; Merz & Kendoul, 2011; Scherer et al., 2008; Tsenkov et al., 2008), of UAS guidance have been of the hierarchical decoupled control type, or in some cases focus only on the local planning needed to…”