With the rapid development of smartphone technology, pedestrian navigation based on built-in inertial sensors in smartphones shows great application prospects. Currently, most smartphone-based pedestrian dead reckoning (PDR) algorithms normally require a user to hold the phone in a fixed mode and, thus, need to correct the gyroscope heading with inputs from other sensors, which restricts the viability of pedestrian navigation significantly. In this paper, in order to improve the accuracy of the traditional step detection and step length estimation method for different users, a state transition-based step detection method and a step length estimation method using a neural network are proposed. In order to decrease the heading errors and inertial sensor errors in multi-mode system, a multi-mode intelligent recognition method based on a neural network was constructed. On this basis, we propose a heading correction method based on zero angular velocity and an overall correction method based on lateral velocity limitation (LV). Experimental results show that the maximum positioning errors obtained by the proposed algorithm are about 0.9% of the total path length. The proposed novel PDR algorithm dramatically enhances the user experience and, thus, has high value in real applications.

Compared with simultaneous localization and mapping (SLAM) problems based on Lidar or visual sensors, range-only SLAM (RO-SLAM) is lacking bearing information. It brings challenges to particle sampling and SLAM estimation. This article proposes an efficient distributed particle filter (EDPF) for RO-SLAM problems. To overcome the difficulties of sampling in a high-dimensional state space, EDPF is decomposed into a set of subparticle filters (sub-PFs) with low dimensionality. Each sub-PF corresponds with an observed beacon, which directly reduces the sampling complexity. A joint weight update method is proposed to exploit the correlation among sub-PFs. It reweighs the particles via an auxiliary distribution after each sub-PF’s local filtering and embodies a better proposal distribution for distributed filtering. In addition, a beacon diagnosis method is proposed, it can detect and reinitialize the wrong converged beacon position estimates, which further reduces the SLAM error accumulation problem. We consider a RO-SLAM system with an odometer and ultrawideband (UWB) to verify the proposed EDPF. Results show that EDPF outperforms many existing RO-SLAM methods, which obtains the best performance with the acceptable computational load.

The combination of biomechanics and inertial pedestrian navigation research provides a very promising approach for pedestrian positioning in environments where Global Positioning System (GPS) signal is unavailable. However, in practical applications such as fire rescue and indoor security, the inertial sensor-based pedestrian navigation system is facing various challenges, especially the step length estimation errors and heading drift in running and sprint. In this paper, a trinal-node, including two thigh-worn inertial measurement units (IMU) and one waist-worn IMU, based simultaneous localization and occupation grid mapping method is proposed. Specifically, the gait detection and segmentation are realized by the zero-crossing detection of the difference of thighs pitch angle. A piecewise function between the step length and the probability distribution of waist horizontal acceleration is established to achieve accurate step length estimation both in regular walking and drastic motions. In addition, the simultaneous localization and mapping method based on occupancy grids, which involves the historic trajectory to improve the pedestrian’s pose estimation is introduced. The experiments show that the proposed trinal-node pedestrian inertial odometer can identify and segment each gait cycle in the walking, running, and sprint. The average step length estimation error is no more than 3.58% of the total travel distance in the motion speed from 1.23 m/s to 3.92 m/s. In combination with the proposed simultaneous localization and mapping method based on the occupancy grid, the localization error is less than 5 m in a single-story building of 2643.2 m${}^2$.

This article provides a multihypothesis Gaussian belief propagation (MGBP) for radio ranging-based localization and mapping systems. To overcome the problem of lacking bearing information, MGBP is implemented via a new multihypothesis modeled message passing process, which can fully represent the beacon uncertainties in a factor graph. In addition, MGBP does not need an external estimator to initialize the estimated states, which shows a more concise computational framework compared with the existing graph optimization methods. In the simulation and field trial dataset based on the ultrawide bandwidth (UWB) ranging, the proposed MGBP shows a better performance than many other existing range-only simultaneous localization and mapping (RO-SLAM) methods. The MGBP framework can be further adapted to other problems that need particle approximations or reasonable initial values, such as navigation problems with strong nonlinearity or high state uncertainty.

Indoor localization using mobile sensors has gained momentum lately. However, indoor pedestrian localization with low-cost inertial measurement unit (IMU) has not been practically applied, and the main challenge is that it is difficult for low-cost IMU to obtain high-precision localization performance over a long period of time. In this article, we propose a multisource fusion indoor pedestrian autonomous localization framework combining landmark maps and occupancy grid maps. Combining the ideas of optimization and occupancy grid map-based inertial simultaneous localization and mapping (OGI-SLAM) algorithm, we maintain the short- and medium-term localization accuracy with OGI-SLAM and achieve long-term localization accuracy improvement with keypoint maps. Without prior knowledge of the map, we use user motion features and environmental features to extract keypoints in the trajectory and construct keypoint descriptors. Combining the uncertainty of system location and the similarity of environmental features, we propose a keypoint extraction and association method to solve the problem of inaccurate particle filtering loop closing with low-cost IMU. In addition, a two-step trajectory optimization method is proposed, which uses bundle adjustment to correct the positioning error of keypoints and models the error correction of common points between keypoints as affine transformation, avoiding the large matrix inversion operation in the optimization process. Simulations and field trial evaluations show that the system is able to achieve a maximum positioning accuracy of 1.64 m with low-performance IMU on board, representing a 57% maximum improvement in positioning accuracy compared to OGI-SLAM. This highlights the promise of OGI-SLAM2 as an unconventional indoor positioning method.

Collaborative indoor positioning techniques for pedestrians have been extensively researched in the past years, particularly concerning the range-based collaborative indoor positioning system. However, range-based indoor collaboration methods suffer from nonline-of-sight (NLOS) and electromagnetic signal loss in indoor environments. Meanwhile, these methods require prior knowledge of the initial relative pose of the pedestrian, which is difficult to obtain in such an environment. To overcome the unreliable mutual observation and the lack of initial relative poses information in collaborative position systems, this article proposes a distributed collaborative inertial simultaneous localization and mapping (DCOGI-SLAM) framework for collaborative pedestrian positioning systems in unknown indoor environments without prior information. A coarse alignment method of relative poses based on encounter events is proposed to obtain the initial relative poses of pedestrians. A mutual position observation is constructed based on the map constructed through the occupancy grid-based inertial SLAM (OGI-SLAM) method to provide a stable innovation for collaborative correction of positioning errors, insulating the system from the NLOS and the loss of ranging signals. Moreover, a distributed collaborative occupancy grid-based inertial simultaneous localization and mapping (DCOGI-SLAM) framework is proposed to enable each pedestrian positioning system in a formation to operate independently. In a two-person collaborative experiment over a space of approximately 2500 m2, the proposed system can obtain comparable accuracy to single OGI-SLAM with known initial relative position information, when the initial relative position information is unknown. The average positioning error of the proposed method is 1.48 m.