Structure from Motion Causally Integrated Over Time

By Alessandro Chiuso, Paolo Favaro, Hailin Jin, Stefano Soatto. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002

393 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/c294633f6ff6f0f9dcc01509b2ac4195a3d5b807

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In Computer Vision and Pattern Recognition

In Media Technology

Citations in the corpus

ORB-SLAM: A Versatile and Accurate Monocular SLAM System (2015, methodology): “Monocular SLAM was initially solved by filtering [20], [21], [29], [30].”
Large-scale direct SLAM with stereo cameras (2015, methodology): “Chiuso et al. [2] proposed one of the first real-time capable monocular SLAM methods based on non-linear filtering.”
Visual-inertial navigation, mapping and localization: A scalable real-time causal approach (2011, methodology): “In our real-time implementation we use a robust filter, as in Chiuso et al. (2002), that is significantly simpler than acceptance or rejection sampling, such as Vedaldi et al. (2005), and proves sufficient for our purpose.”
Visual-Inertial Sensor Fusion: Localization, Mapping and Sensor-to-Sensor Self-calibration (2011, methodology): “Following Chiuso et al. (2002), it is possible to make monocular SFM observable by fixing the directions to three non-collinear features in the first camera image.”
Stereo DSO: Large-Scale Direct Sparse Visual Odometry with Stereo Cameras (2017, methodology): “The first real-time capable visual SLAM and odometry systems were pioneered around 2000 [2, 24, 5, 3].”
A Multi-State Constraint Kalman Filter for Vision-aided Inertial Navigation (2007, background): “, [5], [6], and references therein), with the difference that IMU measurements, instead of a statistical motion model, are used for state propagation.”
Large-scale direct SLAM for omnidirectional cameras (2015, neutral): “This can be done in a filtering framework [3], [4], [5], or in a keyframe-based non-linear optimization framework [6], [7], [8].”
Visual SLAM: Why filter? (2012, neutral): “Two methodologies have been prevalent: filtering approaches [1, 3, 9, 17, 6] which fuse measurements from all images sequentially by updating probability distributions over features and camera pose parameters; and Bundle Adjustment (BA) methods which perform batch optimisation over selected images f”