Monocular Omnidirectional Visual Odometry for Outdoor Ground Vehicles

By Davide Scaramuzza, Roland Yves Siegwart. 2008

319 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Geology

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Citations in the corpus

A Survey of Autonomous Driving: Common Practices and Emerging Technologies (2019, methodology): “The reminder of this section details the three most common approaches that use solely on-board sensors: Global Positioning System and Inertial Measurement Unit (GPS-IMU) fusion, Simultaneous Localization And Mapping (SLAM), and state-of-the-art a priori map-based localization.”
Scale Invariant Feature Transform on the Sphere: Theory and Applications (2011, background): “This kind of sensors has a lot of applications, such as video surveillance (Boult et al. 2001) or object tracking (Chen et al. 2008), and their use has become very common in robot navigation (Menegatti et al. 2006) and in autonomous vehicles (Ehlgen et al. 2008; Scaramuzza and Siegwart 2008).”
Graph-Based Classification of Omnidirectional Images (2017, methodology): “For example, images from ﬁsheye [16], which can reach ﬁeld of view (FOV) of more than 180 °, or omnidirectional cameras, that provide 360 ° FOV [17, 18] are widely used in virtual reality and robotics [17, 19] applications.”
Large-scale direct SLAM for omnidirectional cameras (2015, methodology): “For instance [11] uses a catadioptric system to estimates the ego-motion of a vehicle, using direct photometric error minimization for rotation estimation – it is however restricted to planar motion.”
An Overview to Visual Odometry and Visual SLAM: Applications to Mobile Robotics (2015, methodology): “Other research involving VO was performed by Scaramuzza and Siegwart [102] where they focus on VO for ground vehicles in an outdoor environment using a monocular omni-directional camera and fuse the motion estimates gained by two following approaches.”
Visual simultaneous localization and mapping: a survey (2012, background): “2004) or omnidirectional (Scaramuzza and Siegwart 2008) with the goal of increasing visual range and thus decrease, to some extent, the cumulative error of pose estimation.”
Real-time monocular visual odometry for on-road vehicles with 1-point RANSAC (2009, methodology): “Successful results with only a single camera and over long distances (from hundreds of meters up to kilomeers) have been obtained in the last decade using both perspective and omnidirectional cameras (see [2]–[9]).”
Computer vision in automated parking systems: Design, implementation and challenges (2017, methodology): “1, via visual SLAM/bundle adjustment techniques [25], although there are other methods for visual odometry [26].”
Computer Vision for Autonomous Vehicles: Problems, Datasets and State-of-the-Art (2017, background): “Scaramuzza & Siegwart (2008) estimate the ego-motion of the vehicle relative to the road from a single, central omnidirectional camera by using a homography based tracker for the ground plane and an appearance-based tracker for the rotation of the vehicle.”