Parallel Tracking and Mapping on a camera phone

By Georg S. W. Klein, David William Murray. 2009 8th IEEE International Symposium on Mixed and Augmented Reality, 2009

624 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/82bd97c7f7fbd62b89f72091613b9846977fd731

How researchers see this paper

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In Aerospace Engineering

In Computer Vision and Pattern Recognition

Citations in the corpus

First-person hyper-lapse videos (2014, methodology): “We expect that substantial speedups are possible by replacing the incremental SfM reconstruction with a real-time SLAM system [Klein and Murray 2009], and finding a faster heuristic for preventing selecting of occluded scene parts in the source selection.”
Pose Estimation for Augmented Reality: A Hands-On Survey (2016, positive): “on a PC [57] and on mobile devices [58].”
Visual SLAM: Why filter? (2012, positive): “The other class is based on keyframe BA, introduced and mainly dominated by Klein and Murray’s Parallel Tracking and Mapping (PTAM) framework [28].”
PL-SLAM: A Stereo SLAM System Through the Combination of Points and Line Segments (2017, background): “ometric information of the environment and build a physically meaningful map of it [6], [7].”
Scale Drift-Aware Large Scale Monocular SLAM (2010, methodology): “Very recently, a similar method was briefly described by Klein and Murray [15].”
Visual SLAM and Structure from Motion in Dynamic Environments (2018, methodology): “However, since SIFT and SURF are considered computationally expensive, a faster approach such as Features from Accelerated Segment Test (FAST) [130] is utilized for real-time applications [76, 94].”
From Point Clouds to Mesh Using Regression (2017, methodology): “The map is used to estimate the camera pose relative to the scene map by projecting the map points into the current frame and optimizing a photo metric error [13, 27].”
Revisiting Trends in Augmented Reality Research: A Review of the 2nd Decade of ISMAR (2008–2017) (2018, background): “For instance, Klein and Murray [72], which received the Honorable Mention Award in 2008, presented a compositing rendering method that could generate virtual imagery reﬂecting the visual characteristics of the real imagery captured from low-cost commercial cameras, such as distortions and noise.”
GS-SLAM: Dense Visual SLAM with 3D Gaussian Splatting (2023, background): “The existing real-time dense visual SLAM systems are typically based on discrete handcrafted features or deep-learning embeddings, and follow the mapping and tracking architecture in [14].”
On-Manifold Preintegration for Real-Time Visual--Inertial Odometry (2015, methodology): “Therefore, it has been proposed to discard frames except selected keyframes [24, 32–34] or to run the optimization in a parallel thread, using a tracking”
Privacy Preserving Image-Based Localization (2019, background): “Localizing a device within a scene by computing the camera pose from an image is a fundamental problem in computer vision, with high relevance in applications such as robotics [16, 19, 65], augmented/mixed reality (AR/MR) [36, 46], and structure from motion (SfM) [25, 61, 63].”
NICE-SLAM: Neural Implicit Scalable Encoding for SLAM (2021, methodology): “[19], decomposing the task into mapping and tracking.”
EKLT: Asynchronous Photometric Feature Tracking Using Events and Frames (2018, background): “It is a core building block of numerous applications, such as object tracking [3] or Simultaneous Localization and Mapping (SLAM) [4–7].”
Evaluation of Interest Point Detectors and Feature Descriptors for Visual Tracking (2011, positive): “With slight modifications and concessions regarding robustness, this system was shown to work on a mobile phone (Klein and Murray 2009).”
ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual–Inertial, and Multimap SLAM (2020, background): “PTAM [16]–[18] SLAM FAST Pyramid SSD BA Thumbnail - X - - - - Very Good Fair [19] LSD-SLAM [20], [21] SLAM Edgelets Direct PG FABMAP PG - X X - - - Good Good [22] SVO [23], [24] VO FAST+ Hi.”
Visual SLAM algorithms: a survey from 2010 to 2016 (2017, background): “developed a mobile phone version of PTAM [31].”