Comparing ICP variants on real-world data sets
By François Pomerleau, Francis Colas, Roland Yves Siegwart, Stéphane Magnenat. Autonomous Robots, 2013
788 citations recorded by the source corpus.
Subfields: Aerospace Engineering, Electrical and Electronic Engineering, Geology
View Paper: https://www.semanticscholar.org/paper/7842c2f0dedf9f891685a0abf6958435149eeea6
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Aerospace Engineering
- François Michaud
- Mathieu Labbé
- Ji Zhang
- Sanjiv Singh
- Giorgio Grisetti
- Jacopo Serafin
- Roland Yves Siegwart
- François Pomerleau
In Electrical and Electronic Engineering
- Ji Zhang
- Sanjiv Singh
- François Michaud
- Mathieu Labbé
- Giorgio Grisetti
- Jacopo Serafin
- Roland Yves Siegwart
- Francis Colas
In Geology
- Ji Zhang
- Sanjiv Singh
- François Michaud
- Mathieu Labbé
- Giorgio Grisetti
- Jacopo Serafin
- Roland Yves Siegwart
- Francis Colas
Citations in the corpus
- RTAB‐Map as an open‐source lidar and visual simultaneous localization and mapping library for large‐scale and long‐term online operation (2018, methodology): “(cid:129) ETH Zurich Autonomous Systems Lab (ETHZASL) ‐ ICP ‐ Mapper 4 , based on libpointmatcher library (Pomerleau, Colas, Siegwart, & Magnenat, 2013), can be used to create 2D occupancy grid maps from 2D lidar and an assembled point cloud from 2D or 3D lidars.”
- Visual-lidar odometry and mapping: low-drift, robust, and fast (2015, methodology): “The scan matching involves an iterative closest point method [22], similar to the sweep to sweep refinement step without the motion model.”
- LOAM: Lidar Odometry and Mapping in Real-time (2014, methodology): “In this case, standard ICP methods [12] can be used to match laser returns between different scans.”
- A Review of Point Cloud Registration Algorithms for Mobile Robotics (2015, methodology): “All of those processing steps were made using the library libpointmatcher presented in [Pomerleau et al., 2013].”
- NICP: Dense normal based point cloud registration (2015, methodology): “Point cloud registration has been addressed by many authors and an excellent overview is given by Pomerleau et al. in [1].”
- Image Matching from Handcrafted to Deep Features: A Survey (2020, background): “The reader is referred to a recent survey (Pomerleau et al. 2013) for a detailed discussion of ICP’s variants.”
- Low-drift and real-time lidar odometry and mapping (2017, methodology): “In this case, ICP methods (Pomerleau et al. 2013) can be used to match laser returns between different scans.”