We present a new multi-sensor dataset for multi-view 3D surface reconstruction. It includes registered RGB and depth data from sensors of different resolutions and modalities: smartphones, Intel RealSense, Microsoft Kinect, industrial cameras, and structured-light scanner. The scenes are selected to emphasize a diverse set of material properties challenging for existing algorithms. We provide around 1.4 million images of 107 different scenes acquired from 100 viewing directions under 14 lighting conditions. We expect our dataset will be useful for evaluation and training of 3D reconstruction algorithms and for related tasks. The dataset is available at skol tech3d. appliedai. tech.

Visual localization is a fundamental task for a wide range of applications in the field of robotics. Yet, it is still a complex problem with no universal solution, and the existing approaches are difficult to scale: most state-of-the-art solutions are unable to provide accurate localization without a significant amount of storage space. We propose a hierarchical, low-memory approach to localization based on keypoints with different descriptor lengths. It becomes possible with the use of the developed unsupervised neural network, which predicts a feature pyramid with different descriptor lengths for images. This structure allows applying coarse-to-fine paradigms for localization based on keypoint map, and varying the accuracy of localization by changing the type of the descriptors used in the pipeline. Our approach achieves comparable results in localization accuracy and a significant reduction in memory consumption (up to 16 times) among state-of-the-art methods.

In this study, we propose a novel visual localization approach to accurately estimate six degrees of freedom (6-DoF) poses of the robot within the 3D LiDAR map based on visual data from an RGB camera. The 3D map is obtained utilizing an advanced LiDAR-based simultaneous localization and mapping (SLAM) algorithm capable of collecting a precise sparse map. The features extracted from the camera images are compared with the points of the 3D map, and then the geometric optimization problem is being solved to achieve precise visual localization. Our approach allows employing a scout robot equipped with an expensive LiDAR only once — for mapping of the environment, and multiple operational robots with only RGB cameras onboard — for performing mission tasks, with the localization accuracy higher than common camera-based solutions. The proposed method was tested on the custom dataset collected in the Skolkovo Institute of Science and Technology (Skoltech). During the process of assessing the localization accuracy, we managed to achieve centimeter-level accuracy; the median translation error was as low as 1.3 cm. The precise positioning achieved with only cameras makes possible the usage of autonomous mobile robots to solve the most complex tasks that require high localization accuracy.

Existing Simultaneous Localization and Mapping (SLAM) approaches are limited in their scalability due to growing map size in long-term robot operation. Moreover, processing such maps for localization and planning tasks leads to the increased computational resources required onboard. To address the problem of memory consumption in long-term operation, we develop a novel real-time SLAM algorithm, MeSLAM, that is based on neural field implicit map representation. It combines the proposed global mapping strategy, including neural networks distribution and region tracking, with an external odometry system. As a result, the algorithm is able to efficiently train multiple networks representing different map regions and track poses accurately in large-scale environments. Experimental results show that the accuracy of the proposed approach is comparable to the state-of-the-art methods (on average, 6.6 cm on TUM RGB-D sequences) and outperforms the baseline, iMAP*. Moreover, the proposed SLAM approach provides the most compact-sized maps without details distortion (1.9 MB to store 57 m3) among the state-of-the-art SLAM approaches.

Existing visual SLAM approaches are sensitive to illumination, with their precision drastically falling in dark conditions due to feature extractor limitations. The algorithms currently used to overcome this issue are not able to provide reliable results due to poor performance and noisiness, and the localization quality in dark conditions is still insufficient for practical use. In this paper, we present a novel SLAM method capable of working in low light using Generative Adversarial Network (GAN) preprocessing module to enhance the light conditions on input images, thus improving the localization robustness. The proposed algorithm was evaluated on a custom indoor dataset consisting of 14 sequences with varying illumination levels and ground truth data collected using a motion capture system. According to the experimental results, the reliability of the proposed approach remains high even in extremely low light conditions, providing 25.1% tracking time on darkest sequences, whereas existing approaches achieve tracking only 0.6% of the sequence time.

In the proposed study, we describe the possibility of automated dataset collection using an articulated robot. The proposed technology reduces the number of pixel errors on a polygonal dataset and the time spent on manual labeling of 2D objects. The paper describes a novel automatic dataset collection and annotation system, and compares the results of automated and manual dataset labeling. Our approach increases the speed of data labeling 240-fold, and improves the accuracy compared to manual labeling 13-fold. We also present a comparison of metrics for training a neural network on a manually annotated and an automatically collected dataset.

This paper suggests a novel method for customer behavior analytics and demand distribution based on Radio Frequency Identification (RFID) stocktaking. Existing solutions lack applicability to real-life situations in retailing, which may result in unobservable loss of sales. The proposed solution provides new parameters of demand distribution to the retailer using a mobile robot for autonomous stocktaking of RFID-equipped shopping rooms. Built models depict location-related demand dependencies, the most and the least purchasable areas in a store, and precise localization of lost and moved items. Our research differs from the related works by the sheer size of the underlying data set collected in a real-world environment for more than ten months.

Ability of autonomous vehicles to operate in complex dynamic environments requires, among other things, fast and accurate perception of surroundings, which includes recognition and tracking of traffic signs.For development and testing of modern sophisticated computer vision systems large and diverse datasets are of the major importance. To test the robustness of algorithms, image data with different moving speeds, camera settings, lighting and weather conditions are especially important.In this work we present a comprehensive, lifelike dataset of traffic sign images collected on the Russian winter roads in varying conditions, which include different weather, camera exposure, illumination and moving speeds. The dataset was annotated in accordance with the Russian traffic code. Annotation results and images are published under open CC BY 4.0 license and can be downloaded from the project website: http://oscar.skoltech.ru/.