Monocular Vision for Mobile Robot Localization and Autonomous Navigation

By Eric Royer, Maxime Lhuillier, Michel Dhome, Jean-Marc Lavest. International Journal of Computer Vision, 2007

388 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/0b9c921d0784065629d3e1b3134e20e54d730d99

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In Aerospace Engineering

In Computer Vision and Pattern Recognition

Citations in the corpus

Photometric visual servoing for omnidirectional cameras (2013, methodology): “With respect to previous approaches that rely on 3D reconstruction (e.g., Royer et al. 2007) or even on appearance-based approaches (Segvic et al. 2009), the learning step of the approach is simple.”
SKYLINE2GPS: Localization in urban canyons using omni-skylines (2010, background): “can be classified into ones that use a motion model [2], [4] and the approaches free of motion models [20], [26].”
1-Point RANSAC for extended Kalman filtering: Application to real-time structure from motion and visual odometry (2010, methodology): “In the article (Royer et al., 2007), a monocular camera is carried through an environment and a video is recorded.”
Vehicle localization using mono-camera and geo-referenced traffic signs (2015, methodology): “The quality of these methods is directly related to the precision of GPS measurements.”
Simultaneous Localization and Mapping: A Survey of Current Trends in Autonomous Driving (2017, neutral): “Similarly, in [172], a visual database is first built offline using a hierarchical bundle adjustment method.”
Visual teach and repeat for long‐range rover autonomy (2010, methodology): “By changing this parameter, our system scales smoothly between a complete global reconstruction (Royer et al., 2007) and view-sequenced route representations that match against single images along the path ( ˇ Segvi´c et al., 2009).”
Pose Estimation for Augmented Reality: A Hands-On Survey (2016, neutral): “Such an approach has been successfully used for vehicle localization [110] and augmented reality [81], [143], [144] (see Fig.”
Neural Autonomous Navigation with Riemannian Motion Policy (2019, background): “As a result, image-based navigation receives increasing attention recently [4], [5], [6].”
Active vision in robotic systems: A survey of recent developments (2011, methodology): “2010) Exploration Uncertainty driven (Whaite and Ferrie 1997) Reinforcement learning Reinforcement learning (Royer et al. 2007) Site modeling Prediction and verification (Reed and Allen 2000; Chang and Park 2009; Blaer and Allen 2009; Marchand and Chaumette 1999b) Object modeling Next best view (Ban”