DP-SLAM: A visual SLAM with moving probability towards dynamic environments

Most of the existing visual SLAM methods heavily rely on a static world assumption and easily fail in dynamic environments. Some recent works eliminate the influence of dynamic objects by introducing deep learning-based semantic information to SLAM systems. However such methods suffer from high computational cost and cannot handle unknown objects. In this paper, we propose a real-time semantic RGBD SLAM system for dynamic environments that is capable of detecting both known and unknown moving objects. To reduce the computational cost, we only perform semantic segmentation on keyframes to remove known dynamic objects, and maintain a static map for robust camera tracking. Furthermore, we propose an efficient geometry module to detect unknown moving objects by clustering the depth image into a few regions and identifying the dynamic regions via their reprojection errors. The proposed method is evaluated on public datasets and realworld conditions. To the best of our knowledge, it is one of the first semantic RGB-D SLAM systems that run in real-time on a low-power embedded platform and provide high localization accuracy in dynamic environments.

Towards Real-time Semantic RGB-D SLAM in Dynamic Environments

Localization accuracy is a fundamental requirement for Simultaneous Localization and Mapping (SLAM) systems. Traditional visual SLAM (vSLAM) schemes are usually based upon the assumption of static environments, so they do not perform well in dynamic environments. While a number of vSLAM frameworks have been reported for dynamic environments, the localization accuracy is usually unsatisfactory. In this article, we present a novel motion detection and segmentation method using Red Green Blue-Depth (RGB-D) data to improve the localization accuracy of feature-based RGB-D SLAM in dynamic environments. To overcome the problem due to undersegmentation generated by the semantic segmentation network, a mask inpainting method is developed to ensure the completeness of object segmentation. In the meantime, an optical flow-based motion detection method is proposed to detect dynamic objects from moving cameras, allowing robust detection by removing irrelevant information. Experiments performed on the public Technical University of Munich (TUM) RGB-D data set show that the presented scheme outperforms the state-of-art RGB-D SLAM systems in terms of trajectory accuracy, improving the localization accuracy of RGB-D SLAM in dynamic environments.

Moving Object Segmentation and Detection for Robust RGBD-SLAM in Dynamic Environments

Visual SLAM for robot navigation in healthcare facility

In this paper, we propose a deep learning based assistive system to improve the environment perception experience of visually impaired (VI). The system is composed of a wearable terminal equipped with an RGBD camera and an earphone, a powerful processor mainly for deep learning inferences and a smart phone for touch-based interaction. A data-driven learning approach is proposed to predict safe and reliable walkable instructions using RGBD data and the established semantic map. This map is also used to help VI understand their 3D surrounding objects and layout through well-designed touchscreen interactions. The quantitative and qualitative experimental results show that our learning based obstacle avoidance approach achieves excellent results in both indoor and outdoor datasets with low-lying obstacles. Meanwhile, user studies have also been carried out in various scenarios and showed the improvement of VI's environment perception experience with our system.

/pdf/deep-learning-based-wearable-assistive-system-for-visually-if8yfya6id.pdf

Deep Learning Based Wearable Assistive System for Visually Impaired People

This paper presents a novel framework for simultaneously implementing localization and segmentation, which are two of the most important vision-based tasks for robotics. While the goals and techniques used for them were considered to be different previously, we show that by making use of the intermediate results of the two modules, their performance can be enhanced at the same time. Our framework is able to handle both the instantaneous motion and long-term changes of instances in localization with the help of the segmentation result, which also benefits from the refined 3D pose information. We conduct experiments on various datasets, and prove that our framework works effectively on improving the precision and robustness of the two tasks and outperforms existing localization and segmentation algorithms.

pdf/a-unified-framework-for-mutual-improvement-of-slam-and-3kg75sah15.pdf

A Unified Framework for Mutual Improvement of SLAM and Semantic Segmentation

The embodiments of the invention disclose a display data processing method and apparatus, and relates to the technical field of image processing. The method and the apparatus herein can handle multiple rendering tasks in one rendering process, and save system resources. The method herein includes the following steps: based on received rendering situation of each user, acquiring the user data that is requested in a user rendering request, wherein the user data includes a scene picture and an augmented reality picture; setting layer information for of a virtual camera which conducts image rendering on the scene picture and the augmented reality picture and layer information of the scene picture and the augmented reality picture in the rendering process, wherein the virtual camera and the scene picture and the augmented reality picture which are rendered by the virtual camera correspond to the same layer information in the rendering process; and based on different layer information, through the virtual camera corresponding to the layer information in the rendering process, rendering the scene picture and the augmented reality picture. According to the embodiments of the invention, the method and the apparatus herein are applied to processing display data.

Display data processing method and apparatus

The embodiments of the present invention disclose a training data generating method, a generating apparatus, and an image semantic segmentation method thereof. The training data generating method includes: setting a corresponding category label for an object model in a three-dimensional stereoscopic scene; acquiring a corresponding plurality of scene graphs by changing scene parameters of the three-dimensional stereoscopic scene; and rendering the object model into the monochrome material corresponding to the category label of a model; acquiring a semantic segmentation map corresponding to each scene graph based on the rendered object model; and storing each scene graph and the semantic segmentation map corresponding to the corresponding scene graph as a group of training data. The methodcan quickly generate a large number of scene images and semantic segmentation pictures that meet different usage requirements and meet the requirements of use, greatly saving time and manpower.

Training data generating method, generating device and image semantic segmentation method

A cloud computing-based calibration method, a device, an electronic device, and a computer program product. The method comprises: determining an existing conversion parameter G, the conversion parameter G being a conversion parameter from a tracking coordinate system to a virtual coordinate system produced by calibration of an OSTHMD on the basis of SPAAM; acquiring alignment data of a current user; and computing, on the basis of the alignment data of the current user and of the conversion parameter G, a conversion parameter G' from the tracking coordinate system to the virtual coordinate system of the current user. In the present application, the calibration of the OSTHMD of the current user can be completed by utilizing existing calibration data, the need to perform a full SPAAM calibration with respect to the current user is obviated, and only a small amount of alignment data needs to be collected, thus simplifying operation and reducing the time spent.

Cloud computing-based calibration method, apparatus, electronic device and computer program product

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Papers

A Unified Framework for Mutual Improvement of SLAM and Semantic Segmentation

Display data processing method and apparatus

Training data generating method, generating device and image semantic segmentation method

A Unified Framework for Mutual Improvement of SLAM and Semantic Segmentation

Cloud computing-based calibration method, apparatus, electronic device and computer program product