Visible light positioning (VLP) is widely believed to be a cost-effective answer to the growing demand for service-based indoor positioning. Meanwhile, high accuracy localization is very important for mobile robots in various scenes including industrial, domestic and public transportation workspace. In this paper, an indoor robot VLP localization system based on Robot Operating System (ROS) is presented for the first time, aiming at promoting the application of VLP in mature robotic system. On the basis of our previous researches, we innovatively designed a VLP localization package which contains the basic operation control of the robots, the features extraction and recognition of the LED-ID, cm-level positioning, and robust dynamic tracking algorithms. This package exploited the proposed lightweight algorithm, distributed framework design, the loose coupling characteristics of the ROS, and the message communication methods among different nodes. What's more, an efficient LED-ID detection scheme is proposed to ensure the lightweight and accuracy of the positioning. A prototype system has been implemented on a Turtlebot3 Robot ¹¹ Experiment Demonstration is available at: https://kwanwaipang.github.io/Image/ROS.mp4.. Experimental results show that the proposed system can provide robot indoor positioning accuracy within 1 cm and an average computational time of only 0.08 s.

Recently, VLC has gradually become a research hotspot in indoor environments. But unfortunately, in the matter of algorithm complexity and positioning accuracy, most existing VLC-based systems fail to deliver satisfactory performance. Moreover, the majority of visible light positioning algorithm in them are based on 2-D plane. In addition, some of the systems realize 3-D positioning on the base of various sensors or hybrid complex algorithm. These methods greatly reduce the robustness of VLC system. To solve these problem, a novel VLC positioning system based on modified PSO algorithm is put forward in this article. PSO is a powerful population-based stochastic approach to solve the global optimization problems, such as VLC-based indoor positioning, which can be transformed into a global optimization problem. And the positioning results proves that this system can prove high localization accuracy and significantly lower the algorithm complexity. Moreover, in the experiment, we come up with a solution that using Kalman filter to deal with the unstable received signals. Our experiment result proves the mentioned system satisfies the requirement of cm-level indoor positioning. Therefore, this scheme may be considered as one of the competitive indoor positioning candidates in the future.

Complementary metal-oxide-semiconductor (CMOS) sensor based visible light positioning (VLP) has been widely studied in recent years due to its high robustness and high precision. In most researches about CMOS sensor based VLP, researchers always focus on the high-precision positioning algorithm but ignore that the accuracy of LED-ID detection and recognition plays a more important role in a VLP system. Without the correct recognition of LED-ID, the positioning algorithm would be meaningless no matter how effective it is. In addition, high-precision positioning is not required in most applications since it is enough for people to know just the approximate location. To solve these problems, in this paper, an LED-ID detection and recognition method based on visible light positioning using proximity method is propose. Different from the traditional LED-ID coding and decoding method, we create different features for different LED-ID, and use a machine learning method to identify the LED-ID once the feature extraction and selection of the LED image is achieved with an image processing method. It is the first time the machine learning method is used for LED-ID recognition in VLP. Moreover, we use a proximity-based positioning method to get the approximate location since it is easy to obtain once the LED-ID is recognized. The studies we have demonstrated shows that the proposed method can achieve high LED-ID recognition rate, and provide enough unique LED-ID for variable large-scale indoor VLP system. Furthermore, with the development of camera technology, the number of the unique LED-ID and the maximum recognizable distance would increase. Therefore, this scheme may be considered as one of the useful LED-ID detection and recognition method for visible light positioning in the future.

Robust state estimation in challenge situations is still an unsolved problem, especially achieving onboard pose feedback control for aggressive motion. In this paper, we propose robust and real-time event-based visual-inertial odometry (VIO) that incorporates event, image, and inertial measurements. Our approach utilizes line-based event features to provide additional structure and constraint information in human-made scenes, while point-based event and image features complement each other through well-designed feature management. To achieve reliable state estimation, we tightly couple the point-based and line-based visual residuals from the event camera, the point-based visual residual from the standard camera, and the residual from IMU pre-integration using a keyframe-based graph optimization framework. Experiments in the public benchmark datasets show that our method can achieve superior performance compared with the state-of-the-art image-based or event-based VIO. Furthermore, we demonstrate the effectiveness of our pipeline through onboard closed-loop quadrotor aggressive flight and large-scale outdoor experiments. Videos of the evaluations can be found on our website: https://youtu.be/KnWZ4anBMK4. Note to Practitioners —Driven by the need for real-time closed-loop control for drones under aggressive motion and broad illumination environments, many existing VIO systems fail to meet these requirements due to the inherent limitations of standard cameras. Event cameras are bio-inspired sensors that capture pixel-level illumination changes instead of the intensity image with a fixed frame rate, which can provide reliable visual perception during high-speed motions and in high dynamic range scenarios. Therefore, developing state estimation algorithms based on event cameras offers exciting opportunities for robotics. However, adopting event cameras is challenging due to the event streams being composed of asynchronous events which are fundamentally different from the synchronous intensity images. Moreover, event cameras output minimal information or even noise when the relative motion between the camera and the scene is limited, such as in a still state, while standard cameras can provide rich perception information in most scenarios. In this paper, we propose a robust, high-accurate, and real-time optimization-based monocular event-based VIO framework that tightly fuses the event, image, and IMU measurement together. Owing to the well-designed framework and good feature management, our system can provide robust and reliable state estimation in challenging environments. The efficiency of our system is adequate to achieve real-time operation on platforms with limited resources, such as providing onboard pose feedback for quadrotor flights.

Visible light positioning (VLP) is a promising technology since it can provide high accuracy indoor localization based on the existing lighting infrastructure. However, existing approaches often require dense LED distributions and persistent line-of-sight (LOS) between transmitter and receiver. What's more, sensors are imperfect, and their measurements are prone to errors. Through multi sensors fusion, we can compensate the deficiencies of stand-alone sensors and provide more reliable pose estimations. In this work, we propose a loosely-coupled multi-sensor fusion method based on VLP and Simultaneous Localization and Mapping (SLAM), using light detection and ranging (LiDAR), odometry, and rolling shutter camera. Our multi-sensor localizer can provide accurate and robust robot localization and navigation in LED shortage/outage situations. The experimental results show that our proposed scheme can provide an average accuracy of 2.5 cm with around 42 ms average positioning latency.

Accurate indoor positioning is critical in the field of location-based services and robotics. Visible light positioning (VLP) technology is a promising technique as it can provide high accuracy positioning based on the existing lighting infrastructure. However, it is difficult to meet the requirement of multiple LED anchors in range for successful and accurate positioning. In this article, we proposed a loosely-coupled VLP-inertial fusion method for VLP, with an inertial measurement unit (IMU) and rolling shutter camera, to improve positioning robustness under LED shortage/outage. The efficacy of the proposed VLP scheme as well as the robustness under LED outage, handover situation and background light interference, are verified by real-world experiments. The results show that our proposed scheme can provide an average accuracy of 2.1 cm (stationary localization) and the average computational time in low-cost embedded platforms is around 33 ms.