A stochastic map for uncertain spatial relationships
Proceedings of the 4th international symposium on Robotics Research
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
A Flexible New Technique for Camera Calibration
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fastslam: a factored solution to the simultaneous localization and mapping problem with unknown data association
MonoSLAM: Real-Time Single Camera SLAM
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast incremental square root information smoothing
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Inverse Depth Parametrization for Monocular SLAM
IEEE Transactions on Robotics
Improving odometry using a controlled point laser
Autonomous Robots
Spatio-temporal feature-based keyframe detection from video shots using spectral clustering
Pattern Recognition Letters
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The lack of publicly accessible datasets with a reliable ground truth has prevented in the past a fair and coherent comparison of different methods proposed in the mobile robot Simultaneous Localization and Mapping (SLAM) literature. Providing such a ground truth becomes specially challenging in the case of visual SLAM, where the world model is 3-dimensional and the robot path is 6-dimensional. This work addresses both the practical and theoretical issues found while building a collection of six outdoor datasets. It is discussed how to estimate the 6-d vehicle path from readings of a set of three Real Time Kinematics (RTK) GPS receivers, as well as the associated uncertainty bounds that can be employed to evaluate the performance of SLAM methods. The vehicle was also equipped with several laser scanners, from which reference point clouds are built as a testbed for other algorithms such as segmentation or surface fitting. All the datasets, calibration information and associated software tools are available for download http://babel.isa.uma.es/mrpt/papers/dataset2009/ .