Outdoor SLAM using visual appearance and laser ranging

By Paul M. Newman, Dave Cole, Kin Leong Ho. Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006., 2006

412 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/302d49b194fcd2058ba9194572e147b64aaaf740

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In Aerospace Engineering

In Computer Vision and Pattern Recognition

Citations in the corpus

Multiple‐Robot Simultaneous Localization and Mapping: A Review (2016, neutral): “, 2006) (Newman et al., 2006) (Weingarten and Siegwart, 2006) .”
Perception, Planning, Control, and Coordination for Autonomous Vehicles (2017, methodology): “More elaborate ways of detecting loop closures are also available in the literature, such as by learning from range sensor data [159], or probabilistically from visual appearance [160].”
SeqSLAM: Visual route-based navigation for sunny summer days and stormy winter nights (2012, methodology): “Outside of RatSLAM, the use of sequences, albeit with a feature-based method, was proposed in [20].”
Visual Odometry : Part II: Matching, Robustness, Optimization, and Applications (2012, methodology): “Recently, loop detection by visual similarity using local image descriptors got a lot of attention and one of the most successful methods are based on the so-called visual words [70]–[73].”
Towards semantic maps for mobile robots (2008, methodology): “As these data are much richer in information than the 2D scans mostly used in 2D mapping, slightly different algorithms are used for 3D. Based on consistent 3D scans of the environment, scan matching variants are often applied for constructing a 3D map [13] [18] [26] [30] [36] [40] [44].”
FAB-MAP: Probabilistic Localization and Mapping in the Space of Appearance (2008, methodology): “A series of papers by Ho and Newman (2005b,a) Newman and Ho (2005) Newman et al. (2006) advance similarity-matrix-based techniques by considering the problem of perceptual aliasing.”
Using laser range data for 3D SLAM in outdoor environments (2006, methodology): “We refer the reader to the companion paper [18] for a full description of a robust, multi-modal way to detect and affect loop closure, which is then used in conjunction with the techniques discussed in this paper.”
Active Policy Learning for Robot Planning and Exploration under Uncertainty (2008, methodology): “We focus on feature-based representations because we aim to apply the method to visually guided mobile robots [27, 28, 29].”
Simultaneous localization and mapping (SLAM): part II (2006, methodology): “[39] introduces two significant innovations.”
Simultaneous Localization and Mapping: A Survey of Current Trends in Autonomous Driving (2017, positive): “A coupling of vision and laser has also been proposed in [23].”
Appearance-Based Loop Closure Detection for Online Large-Scale and Long-Term Operation (2013, background): “As an exclusive or complementary alternative, appearance-based loop-closure detection approaches [15]–[19] generally detect a loop closure”