scholarly journals An Enhanced Pedestrian Visual-Inertial SLAM System Aided with Vanishing Point in Indoor Environments

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7428
Author(s):  
Wennan Chai ◽  
Chao Li ◽  
Mingyue Zhang ◽  
Zhen Sun ◽  
Hao Yuan ◽  
...  
Keyword(s):  
Measurement Errors ◽  
Inertial Navigation ◽  
Dead Reckoning ◽  
Optimization Method ◽  
Measurement Unit ◽  
Vanishing Point ◽  
Indoor Environments ◽  
Localization And Mapping ◽  
Loop Detection ◽  
Pedestrian Dead Reckoning

The visual-inertial simultaneous localization and mapping (SLAM) is a feasible indoor positioning system that combines the visual SLAM with inertial navigation. There are accumulated drift errors in inertial navigation due to the state propagation and the bias of the inertial measurement unit (IMU) sensor. The visual-inertial SLAM can correct the drift errors via loop detection and local pose optimization. However, if the trajectory is not a closed loop, the drift error might not be significantly reduced. This paper presents a novel pedestrian dead reckoning (PDR)-aided visual-inertial SLAM, taking advantage of the enhanced vanishing point (VP) observation. The VP is integrated into the visual-inertial SLAM as an external observation without drift error to correct the system drift error. Additionally, the estimated trajectory’s scale is affected by the IMU measurement errors in visual-inertial SLAM. Pedestrian dead reckoning (PDR) velocity is employed to constrain the double integration result of acceleration measurement from the IMU. Furthermore, to enhance the proposed system’s robustness and the positioning accuracy, the local optimization based on the sliding window and the global optimization based on the segmentation window are proposed. A series of experiments are conducted using the public ADVIO dataset and a self-collected dataset to compare the proposed system with the visual-inertial SLAM. Finally, the results demonstrate that the proposed optimization method can effectively correct the accumulated drift error in the proposed visual-inertial SLAM system.

scholarly journals An Orthogonal Weighted Occupancy Likelihood Map with IMU-Aided Laser Scan Matching for 2D Indoor Mapping

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1742 ◽  
Author(s):  
Chuang Qian ◽  
Hongjuan Zhang ◽  
Jian Tang ◽  
Bijun Li ◽  
Hui Liu
Keyword(s):  
Laser Scanner ◽  
Location Based Services ◽  
Experimental Result ◽  
Measurement Unit ◽  
Indoor Environments ◽  
Localization And Mapping ◽  
Map Generation ◽  
Representation Method ◽  
Rigid Body Transformation ◽  
Grid Based

An indoor map is a piece of infrastructure associated with location-based services. Simultaneous Localization and Mapping (SLAM)-based mobile mapping is an efficient method to construct an indoor map. This paper proposes an SLAM algorithm based on a laser scanner and an Inertial Measurement Unit (IMU) for 2D indoor mapping. A grid-based occupancy likelihood map is chosen as the map representation method and is built from all previous scans. Scan-to-map matching is utilized to find the optimal rigid-body transformation in order to avoid the accumulation of matching errors. Map generation and update are probabilistically motivated. According to the assumption that the orthogonal is the main feature of indoor environments, we propose a lightweight segment extraction method, based on the orthogonal blurred segments (OBS) method. Instead of calculating the parameters of segments, we give the scan points contained in blurred segments a greater weight during the construction of the grid-based occupancy likelihood map, which we call the orthogonal feature weighted occupancy likelihood map (OWOLM). The OWOLM enhances the occupancy likelihood map by fusing the orthogonal features. It can filter out noise scan points, produced by objects, such as glass cabinets and bookcases. Experiments were carried out in a library, which is a representative indoor environment, consisting of orthogonal features. The experimental result proves that, compared with the general occupancy likelihood map, the OWOLM can effectively reduce accumulated errors and construct a clearer indoor map.

scholarly journals Multimode Pedestrian Dead Reckoning Gait Detection Algorithm Based on Identification of Pedestrian Phone Carrying Position

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Ying Guo ◽  
Qinghua Liu ◽  
Xianlei Ji ◽  
Shengli Wang ◽  
Mingyang Feng ◽  
...  
Keyword(s):  
Inertial Sensors ◽  
Distance Estimation ◽  
Dead Reckoning ◽  
Principal Component ◽  
Detection Algorithm ◽  
Estimation Accuracy ◽  
Indoor Environments ◽  
Using Data ◽  
The Time Domain ◽  
Pedestrian Dead Reckoning

Pedestrian dead reckoning (PDR) is an essential technology for positioning and navigation in complex indoor environments. In the process of PDR positioning and navigation using mobile phones, gait information acquired by inertial sensors under various carrying positions differs from noise contained in the heading information, resulting in excessive gait detection deviation and greatly reducing the positioning accuracy of PDR. Using data from mobile phone accelerometer and gyroscope signals, this paper examined various phone carrying positions and switching positions as the research objective and analysed the time domain characteristics of the three-axis accelerometer and gyroscope signals. A principal component analysis algorithm was used to reduce the dimension of the extracted multidimensional gait feature, and the extracted features were random forest modelled to distinguish the phone carrying positions. The results show that the step detection and distance estimation accuracy in the gait detection process greatly improved after recognition of the phone carrying position, which enhanced the robustness of the PDR algorithm.

scholarly journals Multi-Floor Indoor Pedestrian Dead Reckoning with a Backtracking Particle Filter and Viterbi-Based Floor Number Detection

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4565
Author(s):  
Cedric De Cock ◽  
Wout Joseph ◽  
Luc Martens ◽  
Jens Trogh ◽  
David Plets
Keyword(s):  
Particle Filter ◽  
Real Time ◽  
Clustering Algorithm ◽  
Dead Reckoning ◽  
Detection Algorithm ◽  
Measurement Unit ◽  
Batch Mode ◽  
Access Points ◽  
Median Position ◽  
Pedestrian Dead Reckoning

We present a smartphone-based indoor localisation system, able to track pedestrians over multiple floors. The system uses Pedestrian Dead Reckoning (PDR), which exploits data from the smartphone’s inertial measurement unit to estimate the trajectory. The PDR output is matched to a scaled floor plan and fused with model-based WiFi received signal strength fingerprinting by a Backtracking Particle Filter (BPF). We proposed a new Viterbi-based floor detection algorithm, which fuses data from the smartphone’s accelerometer, barometer and WiFi RSS measurements to detect stairs and elevator usage and to estimate the correct floor number. We also proposed a clustering algorithm on top of the BPF to solve multimodality, a known problem with particle filters. The proposed system relies on only a few pre-existing access points, whereas most systems assume or require the presence of a dedicated localisation infrastructure. In most public buildings and offices, access points are often available at smaller densities than used for localisation. Our system was extensively tested in a real office environment with seven 41 m × 27 m floors, each of which had two WiFi access points. Our system was evaluated in real-time and batch mode, since the system was able to correct past states. The clustering algorithm reduced the median position error by 17% in real-time and 13% in batch mode, while the floor detection algorithm achieved a 99.1% and 99.7% floor number accuracy in real-time and batch mode, respectively.

scholarly journals A Multi-Floor Indoor Pedestrian Localization Method Using Landmarks Detection for Different Holding Styles

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Khanh Nguyen-Huu ◽  
Seon-Woo Lee
Keyword(s):  
Detection Method ◽  
Dead Reckoning ◽  
Superior Performance ◽  
Detection Methods ◽  
Portable Devices ◽  
Indoor Environments ◽  
Fusion Method ◽  
Localization Method ◽  
Pedestrian Dead Reckoning ◽  
Over Time

The pedestrian dead reckoning (PDR) technique is widely used due to its ease of implementation on portable devices such as smartphones. However, the position error that accumulates over time is the main drawback of this technology. In this paper, we propose a fusion method combining a PDR technique and the landmark recognition methods for multi-floor indoor environments using a smartphone in different holding styles. The proposed method attempts to calibrate the position of a pedestrian by detecting whether the pedestrian passes by specific locations called landmarks. Three kinds of landmarks are defined, which are the WiFi, the turning, and the stairs landmarks, and the detection methods for each landmark are proposed. Besides, an adaptive floor detection method using a barometer and a WiFi fingerprinting technique is suggested for tracking a pedestrian in a multi-floor building. The developed system can track the pedestrian holding a smartphone in four styles. The results of the experiment conducted by three subjects changing the holding style in a three-floor building show the superior performance of the proposed method. It reduces the error rate of positioning results to less than 57.51% compared with the improved PDR alone system.

scholarly journals UWB/PDR Tightly Coupled Navigation with Robust Extended Kalman Filter for NLOS Environments

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Xin Li ◽  
Yan Wang ◽  
Kourosh Khoshelham
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Dead Reckoning ◽  
Step Length ◽  
Noise Signal ◽  
Ultra Wideband ◽  
Measurement Unit ◽  
Indoor Environments ◽  
Fusion Algorithm ◽  
Tightly Coupled

The fusion of ultra-wideband (UWB) and inertial measurement unit (IMU) is an effective solution to overcome the challenges of UWB in nonline-of-sight (NLOS) conditions and error accumulation of inertial positioning in indoor environments. However, existing systems are based on foot-mounted or body-worn IMUs, which limit the application of the system to specific practical scenarios. In this paper, we propose the fusion of UWB and pedestrian dead reckoning (PDR) using smartphone IMU, which has the potential to provide a universal solution to indoor positioning. The PDR algorithm is based on low-pass filtering of acceleration data and time thresholding to estimate the step length. According to different movement patterns of pedestrians, such as walking and running, several step models are comparatively analyzed to determine the appropriate model and related parameters of the step length. For the PDR direction calculation, the Madgwick algorithm is adopted to improve the calculation accuracy of the heading algorithm. The proposed UWB/PDR fusion algorithm is based on the extended Kalman filter (EKF), in which the Mahalanobis distance from the observation to the prior distribution is used to suppress the influence of abnormal UWB data on the positioning results. Experimental results show that the algorithm is robust to the intermittent noise, continuous noise, signal interruption, and other abnormalities of the UWB data.

scholarly journals Optimized LOAM Using Ground Plane Constraints and SegMatch-Based Loop Detection

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5419 ◽  
Author(s):  
Xiao Liu ◽  
Lei Zhang ◽  
Shengran Qin ◽  
Daji Tian ◽  
Shihan Ouyang ◽  
...  
Keyword(s):  
Point Cloud ◽  
Ground Plane ◽  
Mapping Method ◽  
Measurement Unit ◽  
Neighborhood Search ◽  
Ground Vehicles ◽  
Map Construction ◽  
Localization And Mapping ◽  
Loop Detection ◽  
3D Lidar

Reducing the cumulative error in the process of simultaneous localization and mapping (SLAM) has always been a hot issue. In this paper, in order to improve the localization and mapping accuracy of ground vehicles, we proposed a novel optimized lidar odometry and mapping method using ground plane constraints and SegMatch-based loop detection. We only used the lidar point cloud to estimate the pose between consecutive frames, without any other sensors, such as Global Positioning System (GPS) and Inertial Measurement Unit (IMU). Firstly, the ground plane constraints were used to reduce matching errors. Then, based on more accurate lidar odometry obtained from lidar odometry and mapping (LOAM), SegMatch completed segmentation matching and loop detection to optimize the global pose. The neighborhood search was also used to accomplish the loop detection task in case of failure. Finally, the proposed method was evaluated and compared with the existing 3D lidar SLAM methods. Experiment results showed that the proposed method could realize low drift localization and dense 3D point cloud map construction.

scholarly journals Combining a Modified Particle Filter Method and Indoor Magnetic Fingerprint Map to Assist Pedestrian Dead Reckoning for Indoor Positioning and Navigation

Sensors ◽  
2019 ◽  
Vol 20 (1) ◽  
pp. 185
Author(s):  
Fang-Shii Ning ◽  
Yu-Chun Chen
Keyword(s):  
Magnetic Field ◽  
Particle Filter ◽  
Dead Reckoning ◽  
Step Length ◽  
Indoor Positioning ◽  
Global Navigation Satellite Systems ◽  
Filter Method ◽  
Indoor Environments ◽  
Positioning Accuracy ◽  
Pedestrian Dead Reckoning

Although advancement has been observed in global navigation satellite systems and these systems are widely used, they cannot provide effective navigation and positioning services in covered areas and areas that lack strong signals, such as indoor environments. Therefore, in recent years, indoor positioning technology has become the focus of research and development. The magnetic field of the Earth is quite stable in an open environment. Due to differences in building and internal structures, this type of three-dimensional vector magnetic field is widely available indoors for indoor positioning. A smartphone magnetometer was used in this study to collect magnetic field data for constructing indoor magnetic field maps. Moreover, an acceleration sensor and a gyroscope were used to identify the position of a mobile phone and detect the number of steps travelled by users with the phone. This study designed a procedure for measuring the step length of users. All obtained information was input into a pedestrian dead reckoning (PDR) algorithm for calculating the position of the device. The indoor positioning accuracy of the PDR algorithm was optimised using magnetic gradients of magnetic field maps with a modified particle filter algorithm. Experimental results reveal that the indoor positioning accuracy was between 0.6 and 0.8 m for a testing area that was 85 m long and 33 m wide. This study effectively improved the indoor positioning accuracy and efficiency by using the particle filter method in combination with the PDR algorithm with the magnetic fingerprint map.

Vision and laser fused SLAM in indoor environments with multi-robot system

2019 ◽  
Vol 39 (2) ◽  
pp. 297-307 ◽  
Author(s):  
Haoyao Chen ◽  
Hailin Huang ◽  
Ye Qin ◽  
Yanjie Li ◽  
Yunhui Liu
Keyword(s):  
Large Scale ◽  
Feature Detection ◽  
Mobile Robotics ◽  
Least Square ◽  
Indoor Environments ◽  
Fusion Algorithm ◽  
Content Type ◽  
Localization And Mapping ◽  
Loop Detection ◽  
Multi Robot

Purpose Multi-robot laser-based simultaneous localization and mapping (SLAM) in large-scale environments is an essential but challenging issue in mobile robotics, especially in situations wherein no prior knowledge is available between robots. Moreover, the cumulative errors of every individual robot exert a serious negative effect on loop detection and map fusion. To address these problems, this paper aims to propose an efficient approach that combines laser and vision measurements. Design/methodology/approach A multi-robot visual laser-SLAM is developed to realize robust and efficient SLAM in large-scale environments; both vision and laser loop detections are integrated to detect robust loops. A method based on oriented brief (ORB) feature detection and bag of words (BoW) is developed, to ensure the robustness and computational effectiveness of the multi-robot SLAM system. A robust and efficient graph fusion algorithm is proposed to merge pose graphs from different robots. Findings The proposed method can detect loops more quickly and accurately than the laser-only SLAM, and it can fuse the submaps of each single robot to promote the efficiency, accuracy and robustness of the system. Originality/value Compared with the state of art of multi-robot SLAM approaches, the paper proposed a novel and more sophisticated approach. The vision-based and laser-based loops are integrated to realize a robust loop detection. The ORB features and BoW technologies are further utilized to gain real-time performance. Finally, random sample consensus and least-square methodologies are used to remove the outlier loops among robots.

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