Stereo vision based indoor/outdoor navigation for flying robots

184 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

(CAD)$^2$RL: Real Single-Image Flight without a Single Real Image (2016, methodology): “mapping and localization techniques based on 3D perception, including SLAM [3], depth sensors [44], stereo cameras [37], and monocular cameras using structure from motion [8].”
Learning to fly by crashing (2017, methodology): “Several methods [1], [3], [9]–[13] use range or visual sensors to infer maps of the environment while simultaneously estimating its position in the map.”
Learning for Disparity Estimation Through Feature Constancy (2017, unknown): “It is a core problem for many stereo vision tasks and has numerous applications in areas such as autonomous vehicles [29], robotics navigation [25], and augmented reality [33].”
Unmanned Aerial Vehicles (UAVs): A Survey on Civil Applications and Key Research Challenges (2018, unknown): “Many researchers utilized this method to avoid a collision and to provide fully autonomous UAV flights [317]–[320].”
Advanced closed loop visual navigation for micro aerial vehicles (2014, methodology): “The latter approach has been improved by Schmid et al. (2013) towards an onboard FPGA implementation of the Semi Global Matching algorithm (Hirschmüller, 2008), obtaining dense depth images in real-time.”
Survey on Computer Vision for UAVs: Current Developments and Trends (2017, background): “Figure 7 presents visualized obstacle free paths a) [50],b) [91] c) [92], d) [93].”