Simultaneous localization and mapping (SLAM): part II

By Tim Bailey, H. F. Durrant-Whyte. 2006

2,507 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Artificial Intelligence, Ocean Engineering

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Citations in the corpus

A Survey of Autonomous Driving: Common Practices and Emerging Technologies (2019, background): “Absolute positioning sensors low low low small lowest [90] Odometry/dead reckoning low low low smallest low [91] GPS-IMU fusion medium medium low small low [92] SLAM medium-high medium high large very high [93] A priori Map-based Landmark search high medium high large medium [94], [95] Point cloud m”
The AdobeIndoorNav Dataset: Towards Deep Reinforcement Learning based Real-world Indoor Robot Visual Navigation (2018, methodology): “The depth channel from the RGB-D sensor is used as a laser scanning to localize itself with respect to the laser-scan map [28].”
Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age (2016, neutral): “oses a constraint on a pair of poses. MAP estimation has been proven to be more accurate and efﬁcient than original approaches for SLAM, based on nonlinear ﬁltering. We refer the reader to the survey [13, 86] for an overview on ﬁltering approaches, and to [264] for a comparison between ﬁltering and ”
Visual simultaneous localization and mapping: a survey (2012, background): “Previously, Durrant and Bailey presented a tutorial divided into two parts that summarizes the SLAM problem (Durrant and Bailey 2006; Bailey and Durrant 2006).”
3-D Mapping With an RGB-D Camera (2014, background): “In general, a graph-based SLAM system can be broken up into three modules [27], [28] : Frontend, backend, and final map Fig.”
Aligning Cyber Space With Physical World: A Comprehensive Survey on Embodied AI (2024, neutral): “Six types of questions are designed, involving the comparison of color, distance, and size between multiple objects.”
Visual SLAM algorithms: a survey from 2010 to 2016 (2017, background): “Note that RGB-D SLAM APIs are provided in consumer devices such as Google Tango([2]) and Structure Sensor([3]).”
A survey of uncertainty in deep neural networks (2021, methodology): “In robot perception, several uncertainty-aware methods have been proposed in the past, starting from localization methods [301], [302], [303] to simultaneous localization and mapping (SLAM) frameworks [304], [305], [306], [307].”
SLAM-Aided Stem Mapping for Forest Inventory with Small-Footprint Mobile LiDAR (2015, methodology): “The method combines positioning and mapping problems in a single framework, and SLAM is considered to be an effective method for resolving positioning and environment-recognizing problems and has been investigated in robotics for two decades [22].”
Simultaneous Localization and Mapping: A Survey of Current Trends in Autonomous Driving (2017, neutral): “A quick panorama has been made 10 years ago in [4] and [5].”
Vision-Based Offline-Online Perception Paradigm for Autonomous Driving (2015, background): “Autonomous navigation in urban environments requires a deep and fast understanding of the scene for safe decision making regarding path planning and obstacle avoidance [2, 12, 33].”
Mapping Large Loops with a Single Hand-Held Camera (2007, background): “We present experimental results demonstrating monocular SLAM as a human carries a camera over long walked trajectories in outdoor areas with people and other clutter, even in the more difficult case of forward-looking camera, and show the closing of loops of several hundred meters.”