A real-time algorithm for mobile robot mapping based on rotation-invariant descriptors and iterative close point algorithm

Vokhmintcev A.; K. Yakovlev

doi:10.1007/978-3-319-52920-2_33

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A real-time algorithm for mobile robot mapping based on rotation-invariant descriptors and iterative close point algorithm

P. 353–369.

Vokhmintcev A., Yakovlev K.

Nowadays many algorithms for mobile robot mapping in indoor environments have been created. In this work we use a Kinect 2.0 camera, a visible range cameras Beward B2720 and an infrared camera Flir Tau 2 for building 3D dense maps of indoor environments. We present the RGB-D Mapping and a new fusion algorithm combining visual features and depth information for matching images, aligning of 3D point clouds, a “loop-closure” detection, pose graph optimization to build global consistent 3D maps. Such 3D maps of environments have various applications in robot navigation, real-time tracking, non-cooperative remote surveillance, face recognition, semantic mapping. The performance and computational complexity of the proposed RGB-D Mapping algorithm in real indoor environments is presented and discussed.

Language: English

DOI

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Keywords: Depth map Fusion Histograms of oriented gradients Iterative closest point algorithm Matching algorithm Simultaneous location and mapping

In book

Analysis of Images, Social Networks and Texts. 5th International Conference, AIST 2016, Yekaterinburg, Russia, April 7-9, 2016, Revised Selected Papers. Communications in Computer and Information Science

Vol. 661. , Switzerland: Springer, 2017.

О некоторых типологических характеристиках андийских глагольных систем

Filatov K., Вопросы языкознания 2024 № 6 С. 32–57

Despite the fact that Andic languages (Avar-Andic < East-Caucasian) are widely recognised as agglutinating, their verbal systems barely demonstrate any agglutinating properties. The most important characteristic of these systems is the incomplete orthogonality of grammatical categories, and the related property of semantic non-additivity or morphological idiomaticity. In terms of morphotactic structure, different Andic systems can ...

Added: October 15, 2024

On Reproducing Semi-dense Depth Map Reconstruction using Deep Convolutional Neural Networks with Perceptual Loss

Makarov I., Dmitrii Maslov, Gerasimova O. et al., , in: Proceedings of 27th ACM International Conference on Multimedia.: NY: ACM, 2019. P. 1080–1084.

In our recent papers, we proposed a new family of residual convolutional neural networks trained for semi-dense and sparse depth reconstruction without use of RGB channel. The proposed models can be used in low-resolution depth sensors or SLAM methods estimating partial depth with certain distributions. We proposed using perceptual loss for training depth reconstruction in ...

Added: September 16, 2019

Depth Map Interpolation using Perceptual Loss

Makarov I., Vladimir Aliev, Gerasimova Olga et al., , in: Adjunct Proceedings of 2017 IEEE International Symposium on Mixed and Augmented Reality (ISMAR-Adjunct).: NY: IEEE, 2017. P. 93–94.

In this paper, we discuss a semi-dense depth map interpolation method based on convolutional neural network. We propose a compact neural network architecture with loss function defined as Euclidean distance in the feature space of VGG-16 neural network used for deep visual recognition. The suggested solution shows state-of-art performance on synthetic and real datasets. Together ...

Added: August 5, 2017

Semi-Dense Depth Interpolation using Deep Convolutional Neural Networks

Makarov I., Vladimir Aliev, Olga Gerasimova, , in: Proceedings of the 25th ACM international conference on Multimedia (ACM MM'17), Mountain View, CA USA, 23-27 October 2017.: NY: Association for Computing Machinery (ACM), 2017. P. 1407–1415.

With advances of recent technologies, augmented reality systems and autonomous vehicles gained a lot of interest from academics and industry. Both these areas rely on scene geometry understanding, which usually requires depth map estimation. However, in case of systems with limited computational resources, such as smartphones or autonomous robots, high resolution dense depth map estimation ...

Added: June 25, 2017