Real-time correlative scan matching

By Edwin Olson. IEEE International Conference on Robotics and Automation, 2009

395 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/f07e40a14e4f7e6d32bbaad32fe47cf61713e637

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In Aerospace Engineering

In Computer Vision and Pattern Recognition

Citations in the corpus

Scan Context++: Structural Place Recognition Robust to Rotation and Lateral Variations in Urban Environments (2021, methodology): “Olson proposed correlative scan matching-based loop closure detection for 2D LiDAR [20, 21].”
Autonomous flight in unstructured and unknown indoor environments (2009, methodology): “Edwin Olson for providing us with a reference implementation of his scan-matcher.”
Robust and Precise Vehicle Localization Based on Multi-Sensor Fusion in Diverse City Scenes (2017, background): “and [21] make exhaustive searching over (x, y, h), which causes the computational complexity of O(n(3)).”
Robust localization using 3D NDT scan matching with experimentally determined uncertainty and road marker matching (2017, neutral): “distribution of the scan matching result [17].”
Fast LIDAR localization using multiresolution Gaussian mixture maps (2015, methodology): “We employ techniques presented by Olson [22] to formulate the multiresolution search as a branch-and-bound problem that can guarantee global optimality over our search space.”
Research Advances and Challenges of Autonomous and Connected Ground Vehicles (2019, methodology): “The scan matching can merge the scan data from range sensors by some transformation methods, like iterative closest point (ICP) [517], Hough transform [518], adaptive random walk [519], histogram approaches [520] and correlative scan matching (CSM) [521].”
SLAM-Aided Stem Mapping for Forest Inventory with Small-Footprint Mobile LiDAR (2015, background): “, classical Iterative Closest Point (ICP) [23], Iterative Closest Line (ICL) [24], Monte Carlo [25], and Maximum Likelihood Estimation (MLE) [26,27].”
A flexible and scalable SLAM system with full 3D motion estimation (2011, methodology): “The real-time correlative scan matching approach [13] uses an exhaustive sampling based approach for scan matching.”
Visual localization within LIDAR maps for automated urban driving (2014, methodology): “The measurement zk is exactly the output of our image registration in (10) and Rk is estimated by fitting a covariance to the explored cost surface, as is done in [23].”
A Tutorial on Graph-Based SLAM (2010, methodology): “We use a variant of the scan-matcher described by Olson [26].”
A Fully Autonomous Indoor Quadrotor (2012, methodology): “…(3) a Linux-based Gumstix embedded PC with USB interfaces and a WiFi network card that communicates with the microcontroller on the quadrotor via an RS-232 interface, (4) and a mirror that is used to deflect some of the laser beams along the z-direction to measure the distance to the ground.”
Real-time loop closure in 2D LIDAR SLAM (2016, methodology): “The covariance matrices Σ ij can be evaluated, for example, following the approach in [15], or locally using the covariance estimation feature of Ceres [14] with (CS).”
The three-dimensional normal-distributions transform : an efficient representation for registration, surface analysis, and loop detection (2009, methodology): “Olson [83] recently presented a 2D scan-matching method in the same vein.”
Efficient Sparse Pose Adjustment for 2D mapping (2010, methodology): “More recently, the correlation method of Konolige and Chou [17], extended by Olson [22], provides an efficient method for finding the globally best match within a given range, while returning TABLE II”