The Fine Time Measurement (FTM) protocol introduced by IEEE 802.11 includes a new ranging method, named Wi-Fi Round Trip Time (Wi-Fi RTT), which can be used for indoor localization. Pedestrian Dead Reckoning (PDR) can provide accurate pedestrian tracking through inertial sensors in a short time. Information fusion of PDR and existing wireless technology is widely used in indoor localization to ensure the robustness and stability. In this paper, we propose a fusion indoor localization method of Wi-Fi RTT and PDR. Firstly, an adaptive filtering system consisting of multiple Extended Kalman Filter (EKF) and a new outlier detection method is proposed to reduce the localization error of Wi-Fi RTT. Secondly, the fusion algorithm based on the Federated Filter (FF) and observability is designed to combine Wi-Fi RTT with PDR. Finally, to further improve the localization performance of the fusion algorithm, a real-time smoothing method with fixed interval is used. We evaluate the proposed method in four different scenarios. The results show that the proposed indoor localization method has better stability and robustness, and the average localization error decreased by 37.4-67.6% compared with the classic EKF-based method.

The Lidar Simultaneous Localization and Mapping (Lidar-SLAM) processes the point cloud from the Lidar and accomplishes location and mapping. Lidar SLAM is usually divided to front-end odometry and back-end optimization, which can run parallelly to improve computation efficiency. The font-end odometry estimates the Lidar motion through processing the point clouds and the Normal Distributions Transform (NDT) algorithm is usually utilized in the point clouds registration. In this paper, with the aim to reduce the accumulated errors, we proposed a weighted NDT combined with a Local Feature Adjustment (LFA) to process the point clouds and improve the accuracy. Cells of the NDT are weighted according to the range’s values and their surface characteristics, the new cost functions with weight are constructed. In the experiments, we tested NDT-LOAM on the KITTI odometry dataset and compared it with the state-of-the-art algorithm ALOAM/LOAM. NDT-LOAM had 0.899% average drift in translation, better than ALOAM and at the level of LOAM; moreover, NDT-LOAM can run at 10 Hz in real-time, while LOAM runs at 1 Hz. The results display that NDT-LOAM is a real-time and low-drift method with high accuracy. In addition, the source code is uploaded to GitHub and the download link is https://github.com/BurryChen/lv_slam .

The introduction of data-driven inertial navigation provides new opportunities that the pedestrian dead reckoning could not well provide for constraining inertial system error drift on smartphones, and has been considered as another promising approach to meet the requirement of location-based services. However, indoor localization systems based on a single technology still have their limitations, such as the drift of inertial navigation and the received signal strength fluctuation of Bluetooth, making them unable to provide reliable positioning. To exploit the complementary strengths of each technology, this paper proposes a feasible fusion framework by utilizing a particle filter to integrate data-driven inertial navigation with localization based on Bluetooth Low Energy (BLE). For data-driven inertial navigation, under the premise of using the deep neural network with great potential in model-free generalization to regress pedestrian motion characteristics, we effectively combined the method of using gravity to stabilize inertial measurement units data to make the network more robust. Experimental results show that in the test of different smartphone usages, the proposed data-driven inertial navigation and BLE-based localization technology have good results in modeling user’s movement and positioning respectively. And due to this, the proposed fusion algorithm has almost unaffected by the usages of smartphones. Compared with BLE-based localization that achieved a good mean positional error (MPE) of 1.76m, for the four usages of texting, swinging, calling and pocket, the proposed fusion algorithm reduced the MPE by 32.35%, 20.51%, 20.74%, and 45.37%, respectively, and can further improve localization accuracy on the basis of existing fusion method.

In the indoor environment, the activity of the pedestrian can reflect some semantic information. These activities can be used as the landmarks for indoor localization. In this paper, we propose a pedestrian activities recognition method based on a convolutional neural network. A new convolutional neural network has been designed to learn the proper features automatically. Experiments show that the proposed method achieves approximately 98% accuracy in about 2 s in identifying nine types of activities, including still, walk, upstairs, up elevator, up escalator, down elevator, down escalator, downstairs and turning. Moreover, we have built a pedestrian activity database, which contains more than 6 GB of data of accelerometers, magnetometers, gyroscopes and barometers collected with various types of smartphones. We will make it public to contribute to academic research.

Every year, for ten years now, the IPIN competition has aimed at evaluating real-world indoor localisation systems by testing them in a realistic environment, with realistic movement, using the EvAAL framework. The competition provided a unique overview of the state-of-the-art of systems, technologies, and methods for indoor positioning and navigation purposes. Through fair comparison of the performance achieved by each system, the competition was able to identify the most promising approaches and to pinpoint the most critical working conditions. In 2020, the competition included 5 diverse off-site off-site Tracks, each resembling real use cases and challenges for indoor positioning. The results in terms of participation and accuracy of the proposed systems have been encouraging. The best performing competitors obtained a third quartile of error of 1 m for the Smartphone Track and 0.5 m for the Foot-mounted IMU Track. While not running on physical systems, but only as algorithms, these results represent impressive achievements.

LiDAR (light detection and ranging), as an active sensor, is investigated in the simultaneous localization and mapping (SLAM) system. Typically, a LiDAR SLAM system consists of front-end odometry and back-end optimization modules. Loop closure detection and pose graph optimization are the key factors determining the performance of the LiDAR SLAM system. However, the LiDAR works at a single wavelength (905 nm), and few textures or visual features are extracted, which restricts the performance of point clouds matching based loop closure detection and graph optimization. With the aim of improving LiDAR SLAM performance, in this paper, we proposed a LiDAR and visual SLAM backend, which utilizes LiDAR geometry features and visual features to accomplish loop closure detection. Firstly, the bag of word (BoW) model, describing the visual similarities, was constructed to assist in the loop closure detection and, secondly, point clouds re-matching was conducted to verify the loop closure detection and accomplish graph optimization. Experiments with different datasets were carried out for assessing the proposed method, and the results demonstrated that the inclusion of the visual features effectively helped with the loop closure detection and improved LiDAR SLAM performance. In addition, the source code, which is open source, is available for download once you contact the corresponding author.