Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Iterative point matching for registration of free-form curves and surfaces
International Journal of Computer Vision
Robot Pose Estimation in Unknown Environments by Matching 2D Range Scans
Journal of Intelligent and Robotic Systems
Calculating Dense Disparity Maps from Color Stereo Images, an Efficient Implementation
International Journal of Computer Vision
Advances in Computational Stereo
IEEE Transactions on Pattern Analysis and Machine Intelligence
Real-Time Localisation and Mapping with Wearable Active Vision
ISMAR '03 Proceedings of the 2nd IEEE/ACM International Symposium on Mixed and Augmented Reality
Lucas-Kanade 20 Years On: A Unifying Framework
International Journal of Computer Vision
Pyro: A python-based versatile programming environment for teaching robotics
Journal on Educational Resources in Computing (JERIC)
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Comparison of Affine Region Detectors
International Journal of Computer Vision
Robotics and Autonomous Systems
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Finding correspondences between sensor measurements obtained at different places is a fundamental task for an autonomous mobile robot. Most matching methods search correspondences between salient features extracted from such measurements. However, finding explicit matches between features is a challenging and expensive task. In this paper we build a local map using a stereo head aided by sonars and propose a method for aligning local maps without searching explicit correspondences between primitives. From objects found by the stereo head, an object probability density distribution is built. Then, the Gauss-Newton algorithm is used to match correspondences, so that, no explicit correspondences are needed.