Autonomous flight in unstructured and unknown indoor environments

By Abraham Bachrach. 2009

123 citations recorded by the source corpus.

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Citations in the corpus

(CAD)$^2$RL: Real Single-Image Flight without a Single Real Image (2016, methodology): “Many of the most successful approaches to indoor navigation have used mapping and localization techniques based on 3D perception, including SLAM [3], depth sensors [33], stereo cameras [27], and monocular cameras using structure from motion [6].”
Learning to fly by crashing (2017, methodology): “Most early research focused on a two-step approach: the first step being perception where either SLAM [1] or sensors [2] are used to estimate the underlying map and/or 3D [3]; the second step is to use the predicted depth or map to issue motor commands”
Natural language command of an autonomous micro-air vehicle (2010, methodology): “Previous work in our group [2] and many others [4], [15], [16] has sought to develop MAVs capable of flight in unstructured indoor and urban canyon GPS-denied environments.”
Vision-Controlled Micro Flying Robots: From System Design to Autonomous Navigation and Mapping in GPS-Denied Environments (2014, background): “Micro helicopters—and notably multi-rotor helicopters— have several advantages compared to fixed-wing micro aerial vehicles: they are able to take off and land vertically, hover on a spot, and even dock to a surface.”
Autonomous, Vision‐based Flight and Live Dense 3D Mapping with a Quadrotor Micro Aerial Vehicle (2016, methodology): “RGB-D sensors for state estimation and mapping with MAVs have been used in Bachrach et al. (2012) as well as in Shen, Michael, & Kumar (2012a, 2012b), where a multifloor autonomous exploration and mapping strategy was presented.”
Monocular‐SLAM–based navigation for autonomous micro helicopters in GPS‐denied environments (2011, methodology): “Bachrach et al. (2009a, 2009b) used an Hokuyo laser scanner and two-dimensional (2D) SLAM for autonomous navigation in a maze.”
Toward a Fully Autonomous UAV: Research Platform for Indoor and Outdoor Urban Search and Rescue (2012, background): “Therefore, the authors in [4] and [5] chose to send laser scanner data to a ground station for processing.”
Opportunities and challenges with autonomous micro aerial vehicles (2012, background): “Initial developments in the area focused on systems capable of navigating indoor environments with algorithms leveraging laser and IMU information to generate a map and localize within the map [4, 14].”
Autonomous multi-floor indoor navigation with a computationally constrained MAV (2011, methodology): “Relevant to this paper is the work of Bachrach et al. [1] [2], Grzonka et al. [3], and Blösch et al. [4] with results toward online autonomous navigation and exploration with an aerial vehicle.”