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${}^2$.

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.

Foot-mounted inertial navigation systems are suitable in GNSS denied environment for special tasks such as fire rescue, soldier, etc. These tasks are often accompanied by vigorous movements such as running and jumping, which is a challenge for the Zero Velocity Update (ZUPT) based inertial navigation system. As the speed increases, the inertial sensor drift increases, while the zero-velocity interval shortens, making the gait detection more difficult and resulting in a limited error correction. And as the motion intensifies, the stability of the system decreases. A multi-node inertial pedestrian navigation system based on the lower limb model constraint is proposed in this paper. Our first contribution in this paper is studying a simplified adaptive zero-bias estimation algorithm. The changes in velocity and angular velocity between two steps are used to estimate and compensate for the drift of accelerometers and gyroscopes. Our second contribution is proposing a novel gait segmentation method by lower limb motion statistics feature. A hybrid detection model of dual IMU is conducted to improve the accuracy of gait recognition. After that, the maximum distance inequality constraint is studied to correct the position error when the still phase is not available in vigorous movement. The final contribution is studying the chi-square fault detection method to improve the stability of KF. Multiple experiments prove that the proposed multi-node inertial pedestrian navigation system is effective in dealing with various challenging in walking, running, and hybrid motion pedestrian inertial navigation problems which could achieve less than 0.3% average distance error.