Using Real-Time Stereo Vision for Mobile Robot Navigation

By D. Murray, James J. Little. Auton. Robots, 2000

506 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/8c268bd2a58a71f467c9a134fc117b539665cf4d

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In Aerospace Engineering

In Computer Vision and Pattern Recognition

Citations in the corpus

Neural Autonomous Navigation with Riemannian Motion Policy (2019, background): “As a result, image-based navigation receives increasing attention recently [4], [5], [6].”
Disparity map quality for image-based rendering based on multiple metrics (2014, background): “Disparity maps can be applied in a wide variety of applications, such as navigation of autonomous robots [6] or depth adjustment for stereoscopic displays [5, 7].”
Review of Stereo Vision Algorithms: From Software to Hardware (2008, background): “Having extracted the disparity map, problems such as 3D reconstruction, positioning, mobile robot navigation, obstacle avoidance, etc., can be dealt with in a more efficient way (Murray and Jennings 1997; Murray and Little 2000).”
3D skeleton-based human action classification: A survey (2016, methodology): “Range images have been studied and used for long time, especially in robotic applications [68 – 70] and scene/object reconstruction [71 – 73].”
Vision-based mobile robot localization and mapping using scale-invariant features (2001, methodology): “As our robot is equipped with Triclops [ 14 ], a trinocular stereo system, the estimated 3D position of the landmarks can be obtained and hence a 3D map can be built and the robot can be localized simultaneously.”
Spectral reflectance at sub‐leaf scale including the spatial distribution discriminating NPK stress characteristics in barley using multiway partial least squares regression (2007, background): “Stereo vision using two or three charged-coupled device (CCD) cameras enables robots to benefit from vision with depth perception (e.g. Murray and Little 2000).”
Learning Depth with Convolutional Spatial Propagation Network (2018, background): “, predicting absolute perpixel distance to the camera has many applications in practice, such as augmented reality (AR), autonomous driving [1], robotics [2], [3], [4].”