Estimating uncertain spatial relationships in robotics
Autonomous robot vehicles
A Method for Registration of 3-D Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
Object modelling by registration of multiple range images
Image and Vision Computing - Special issue: range image understanding
Iterative point matching for registration of free-form curves and surfaces
International Journal of Computer Vision
Using Spin Images for Efficient Object Recognition in Cluttered 3D Scenes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
A decision-theoretic generalization of on-line learning and an application to boosting
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
Relocalisation by Partial Map Matching
Selected Papers from the International Workshop on Sensor Based Intelligent Robots
Robust Real-Time Face Detection
International Journal of Computer Vision
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Robotics and Autonomous Systems
Detecting Loop Closure with Scene Sequences
International Journal of Computer Vision
Scan registration for autonomous mining vehicles using 3D-NDT: Research Articles
Journal of Field Robotics - Special Issue on Mining Robotics
FAB-MAP: Probabilistic Localization and Mapping in the Space of Appearance
International Journal of Robotics Research
Map Matching and Data Association for Large-Scale Two-dimensional Laser Scan-based SLAM
International Journal of Robotics Research
The New College Vision and Laser Data Set
International Journal of Robotics Research
Keypoint design and evaluation for place recognition in 2D lidar maps
Robotics and Autonomous Systems
Journal of Field Robotics - Three-Dimensional Mapping, Part 2
Learning to detect loop closure from range data
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
International Journal of Robotics Research
Exactly Sparse Delayed-State Filters for View-Based SLAM
IEEE Transactions on Robotics
Mapping a Suburb With a Single Camera Using a Biologically Inspired SLAM System
IEEE Transactions on Robotics
Fast and Incremental Method for Loop-Closure Detection Using Bags of Visual Words
IEEE Transactions on Robotics
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In this paper we address the loop closure detection problem in simultaneous localization and mapping ( slam ), and present a method for solving the problem using pairwise comparison of point clouds in both two and three dimensions. The point clouds are mathematically described using features that capture important geometric and statistical properties. The features are used as input to the machine learning algorithm AdaBoost, which is used to build a non-linear classifier capable of detecting loop closure from pairs of point clouds. Vantage point dependency in the detection process is eliminated by only using rotation invariant features, thus loop closure can be detected from an arbitrary direction. The classifier is evaluated using publicly available data, and is shown to generalize well between environments. Detection rates of 66%, 63% and 53% for 0% false alarm rate are achieved for 2D outdoor data, 3D outdoor data and 3D indoor data, respectively. In both two and three dimensions, experiments are performed using publicly available data, showing that the proposed algorithm compares favourably with related work.