On the representation and estimation of spatial uncertainly
International Journal of 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
The digital Michelangelo project: 3D scanning of large statues
Proceedings of the 27th annual conference on Computer graphics and interactive techniques
Globally Consistent Range Scan Alignment for Environment Mapping
Autonomous Robots
IEEE Transactions on Pattern Analysis and Machine Intelligence
FastSLAM: a factored solution to the simultaneous localization and mapping problem
Eighteenth national conference on Artificial intelligence
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
An Introduction to the Kalman Filter
An Introduction to the Kalman Filter
High resolution terrain mapping using low altitude aerial stereo imagery
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Real Time Localization and 3D Reconstruction
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
MonoSLAM: Real-Time Single Camera SLAM
IEEE Transactions on Pattern Analysis and Machine Intelligence
6D SLAM—3D mapping outdoor environments: Research Articles
Journal of Field Robotics
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
DP-SLAM: fast, robust simultaneous localization and mapping without predetermined landmarks
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Adaptive blind deconvolution of linear channels using Renyi's entropy with Parzen window estimation
IEEE Transactions on Signal Processing
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In this paper, we propose and validate a novel approach to solve the Simultaneous Localization and Mapping (SLAM), focused on its application with wearable devices. In order to do so, we use a stereo vision camera as the unique sensor that provides semi-dense information of the environment (appearance and range data). A first approximation of the trajectory is given by an egomotion algorithm, that exploits the information of the stereo observations in order to estimate the action between each pair of consecutive observations (visual odometry). The algorithm provides a locally but not globally consistent approximation because it is only based on local information. In order to obtain a globally consistent map, which is the key topic of this paper, we propose an Information Theory based approach that rectifies the map obtained by the egomotion step by performing successive refinements over the trajectory using global information. The key idea is that the best aligned map is the one with the minimum entropy. In order to ensure the scalability of the algorithm, we propose a dynamic map compression strategy that bounds the complexity of the problem and attenuates both memory and computing time requirements. In the experimental section, we show the results of the algorithm in several situations: structured/unstructured environments, indoor/outdoor scenarios, cyclic/acyclic trajectories, etc. performed with a wearable stereo device that we have built to carry out these experiments.