Recently, with the in-depth development of Industry 4.0 worldwide, mobile robots have become a research hotspot. Indoor localization has become a key component in many fields and the basis for all actions of mobile robots. This paper screened 147 papers in the field of indoor positioning of mobile robots from 2019 to 2021. First, 12 mainstream indoor positioning methods and related positioning technologies for mobile robots are introduced and compared in detail. Then, the selected papers were summarized. The common attributes and laws were discovered. The development trend of indoor positioning of mobile robots is obtained.

Currently, the process of tracking moving objects and determining their indoor location is considered to be one of the most attractive applications that have begun to see widespread use, especially after the adoption of this technology in some smartphone applications. The great developments in electronics and communications systems have provided the basis for tracking and location systems inside buildings, so-called indoor positioning systems (IPSs). The ultra-wideband (UWB) technology is one of the important emerging solutions for IPSs. This radio communications technology provides important characteristics that distinguish it from other solutions, such as secure and robust communications, wide bandwidth, high data rate, and low transmission power. In this paper, we review the implementation of the most important real-time indoor positioning and tracking systems that use ultra-wideband technology for tracking and localizing moving objects. This paper reviews the newest in-market UWB modules and solutions, discussing several types of algorithms that are used by the real-time UWB-based systems to determine the location with high accuracy, along with a detailed comparison that saves the reader a lot of time and effort in choosing the appropriate UWB-module/method/algorithm for real-time implementation.

In recent years the degree of automation in life science laboratories increased considerably by introducing stationary and mobile robots. This trend requires intensified considerations of the occupational safety for cooperating humans, since the robots operate with low volatile compounds that partially emit hazardous vapors, which especially do arise if accidents or leakages occur. For the fast detection of such or similar situations a modular IoT-sensor node was developed. The sensor node consists of four hardware layers, which can be configured individually regarding basic functionality and measured parameters for varying application focuses. In this paper the sensor node is equipped with two gas sensors (BME688, SGP30) for a continuous TVOC measurement. In investigations under controlled laboratory conditions the general sensors’ behavior regarding different VOCs and varying installation conditions are performed. In practical investigations the sensor node’s integration into simple laboratory applications using stationary and mobile robots is shown and examined. The investigation results show that the selected sensors are suitable for the early detection of solvent vapors in life science laboratories. The sensor response and thus the system’s applicability depends on the used compounds, the distance between sensor node and vapor source as well as the speed of the automation systems.

The leakage of gases and chemical vapors is a common accident in laboratory processes that requires a rapid response to avoid harmful effects if humans and instruments are exposed to this leakage. In this paper, the performance of a portable sensor node designed for integration with mobile and stationary robots used to transport chemical samples in automated laboratories was tested and evaluated. The sensor node has four main layers for executing several functions, such as power management, control and data preprocessing, sensing gases and environmental parameters, and communication and data transmission. The responses of three metal oxide semiconductor sensors, BME680, ENS160, and SGP41, integrated into the sensing layer have been recorded for various volumes of selected chemicals and volatile organic compounds, including ammonia, pentane, tetrahydrofuran, butanol, phenol, xylene, benzene, ethanol, methanol, acetone, toluene, and isopropanol. For mobile applications, the sensor node was attached to a sample holder on a mobile robot (ASTI ProBOT L). In addition, the sensor nodes were positioned close to automation systems, including stationary robots. The experimental results revealed that the tested sensors have a different response to the tested volumes and can be used efficiently for hazardous gas leakage detection and monitoring.

Real-time indoor monitoring systems (RTIMS) are a key component of modern technological infrastructures in smart and automated buildings and facilities. They enable the continuous collection, analysis, and response to environmental data under strict time constraints, ensuring optimal system performance. These systems are designed to operate with high accuracy and low latency, making them essential in situations and events where timely decision-making is critical. Their applications range from industrial automation and production line monitoring to smart cities, smart homes, and healthcare for the elderly and disabled. The significant advances in electronics, communications, and software—particularly in Internet of Things (IoT) technologies and data transfer protocols—are reflected in the diversity of real-time monitoring systems, in terms of the parameters that can be monitored, the control and command systems that can be used, and the actuators that respond to commands. In this paper, the concepts, design, components, and working methods of these systems are discussed in detail. The latest research on real-time indoor monitoring systems published over the past five years is reviewed, resulting in the selection of 143 studies that met the inclusion criteria. This review synthesizes the technologies used for data capture, transmission, processing, storage, and visualization, as well as the approaches employed for alerts and system integration. By presenting these technical insights in a structured manner, the article provides a practical reference for researchers and practitioners aiming to design and implement real-time monitoring systems more efficiently and effectively.

We present a novel stereo visual odometry (VO) model that utilizes both optical flow and depth information. While some existing monocular VO methods demonstrate superior performance, they require extra frames or information to initialize the model in order to obtain absolute scale, and they do not take into account moving objects. To address these issues, we have combined optical flow and depth information to estimate ego-motion and proposed a framework for stereo VO using deep neural networks. The model simultaneously generates optical flow and depth information outputs from sequential stereo RGB image pairs, which are then fed into the pose estimation network to achieve final motion estimation. Our experiments have demonstrated that our combination of optical flow and depth information improves the accuracy of camera pose estimation. Our method outperforms existing learning-based and monocular geometry-based methods on the KITTI odometry dataset. Furthermore, we have achieved real-time performance, making our method both effective and efficient.