Research

14 pages, 15837 KiB

Open AccessArticle

Improving Optical Flow Sensor Using a Gimbal for Quadrotor Navigation in GPS-Denied Environment

by Jonathan Flores, Ivan Gonzalez-Hernandez, Sergio Salazar, Rogelio Lozano and Christian Reyes

Sensors 2024, 24(7), 2183; https://doi.org/10.3390/s24072183 - 28 Mar 2024

Viewed by 371

This paper proposes a new sensor using optical flow to stabilize a quadrotor when a GPS signal is not available. Normally, optical flow varies with the attitude of the aerial vehicle. This produces positive feedback on the attitude control that destabilizes the orientation [...] Read more.

This paper proposes a new sensor using optical flow to stabilize a quadrotor when a GPS signal is not available. Normally, optical flow varies with the attitude of the aerial vehicle. This produces positive feedback on the attitude control that destabilizes the orientation of the vehicle. To avoid this, we propose a novel sensor using an optical flow camera with a 6DoF IMU (Inertial Measurement Unit) mounted on a two-axis anti-shake stabilizer mobile aerial gimbal. We also propose a robust algorithm based on Sliding Mode Control for stabilizing the optical flow sensor downwards independently of the aerial vehicle attitude. This method improves the estimation of the position and velocity of the quadrotor. We present experimental results to show the performance of the proposed sensor and algorithms. Full article

(This article belongs to the Special Issue Multi-Sensor Technology for Target Tracking, Positioning and Navigation)

► Show Figures

Figure 1

17 pages, 3587 KiB

Open AccessArticle

Constrained Cubature Particle Filter for Vehicle Navigation

by Li Xue, Yongmin Zhong and Yulan Han

Sensors 2024, 24(4), 1228; https://doi.org/10.3390/s24041228 - 15 Feb 2024

Viewed by 365

Abstract

In vehicle navigation, it is quite common that the dynamic system is subject to various constraints, which increases the difficulty in nonlinear filtering. To address this issue, this paper presents a new constrained cubature particle filter (CCPF) for vehicle navigation. Firstly, state constraints [...] Read more.

In vehicle navigation, it is quite common that the dynamic system is subject to various constraints, which increases the difficulty in nonlinear filtering. To address this issue, this paper presents a new constrained cubature particle filter (CCPF) for vehicle navigation. Firstly, state constraints are incorporated in the importance sampling process of the traditional cubature particle filter to enhance the accuracy of the importance density function. Subsequently, the Euclidean distance is employed to optimize the resampling process by adjusting particle weights to avoid particle degradation. Further, the convergence of the proposed CCPF is also rigorously proved, showing that the posterior probability function is converged when the particle number N → ∞. Our experimental results and the results of a comparative analysis regarding GNSS/DR (Global Navigation Satellite System/Dead Reckoning)-integrated vehicle navigation demonstrate that the proposed CCPF can effectively estimate system state under constrained conditions, leading to higher estimation accuracy than the traditional particle filter and cubature particle filter. Full article

(This article belongs to the Special Issue Multi-Sensor Technology for Target Tracking, Positioning and Navigation)

► Show Figures

Figure 1

13 pages, 3433 KiB

Open AccessArticle

ConGPS: A Smart Container Positioning System Using Inertial Sensor and Electronic Map with Infrequent GPS

by Shan Huang, Zihan Song, Hyung-Rim Choi, Jae-Joong Kim, Do-Myung Park and Byung-Kwon Park

Sensors 2023, 23(22), 9198; https://doi.org/10.3390/s23229198 - 15 Nov 2023

Viewed by 802

Abstract

Real-time global positioning is important for container-based logistics. However, a challenge in real-time global positioning arises from the frequency of both global positioning system (GPS) calls and GPS-denied environments during transportation. This paper proposes a novel system named ConGPS that integrates both inertial [...] Read more.

Real-time global positioning is important for container-based logistics. However, a challenge in real-time global positioning arises from the frequency of both global positioning system (GPS) calls and GPS-denied environments during transportation. This paper proposes a novel system named ConGPS that integrates both inertial sensor and electronic map data. ConGPS estimates the speed and heading direction of a moving container based on the inertial sensor data, the container trajectory, and the speed limit information provided by an electronic map. The directional information from magnetometers, coupled with map-matching algorithms, is employed to compute container trajectories and current positions. ConGPS significantly reduces the frequency of GPS calls required to maintain an accurate current position. To evaluate the accuracy of the system, 280 min of driving data, covering a distance of 360 km, are collected. The results demonstrate that ConGPS can maintain positioning accuracy within a GPS-call interval of 15 min, even if using low-cost inertial sensors in GPS-denied environments. Full article

(This article belongs to the Special Issue Multi-Sensor Technology for Target Tracking, Positioning and Navigation)

► Show Figures

Figure 1

14 pages, 5594 KiB

Open AccessArticle

Application of Adaptive Robust Kalman Filter Base on MCC for SINS/GPS Integrated Navigation

by Linfeng Li, Jian Wang, Zhiming Chen and Teng Yu

Sensors 2023, 23(19), 8131; https://doi.org/10.3390/s23198131 - 28 Sep 2023

Viewed by 638

Abstract

In this paper, an adaptive and robust Kalman filter algorithm based on the maximum correntropy criterion (MCC) is proposed to solve the problem of integrated navigation accuracy reduction, which is caused by the non-Gaussian noise and time-varying noise of GPS measurement in complex [...] Read more.

In this paper, an adaptive and robust Kalman filter algorithm based on the maximum correntropy criterion (MCC) is proposed to solve the problem of integrated navigation accuracy reduction, which is caused by the non-Gaussian noise and time-varying noise of GPS measurement in complex environment. Firstly, the Grubbs criterion was used to remove outliers, which are contained in the GPS measurement. Then, a fixed-length sliding window was used to estimate the decay factor adaptively. Based on the fixed-length sliding window method, the time-varying noises, which are considered in integrated navigation system, are addressed. Moreover, a MCC method is used to suppress the non-Gaussian noises, which are generated with external corruption. Finally, the method, which is proposed in this paper, is verified by the designed simulation and field tests. The results show that the influence of the non-Gaussian noise and time-varying noise of the GPS measurement is detected and isolated by the proposed algorithm, effectively. The navigation accuracy and stability are improved. Full article

(This article belongs to the Special Issue Multi-Sensor Technology for Target Tracking, Positioning and Navigation)

► Show Figures

Figure 1

17 pages, 4761 KiB

Open AccessArticle

Multi-Sensors System and Deep Learning Models for Object Tracking

by Ghina El Natour, Guillaume Bresson and Remi Trichet

Sensors 2023, 23(18), 7804; https://doi.org/10.3390/s23187804 - 11 Sep 2023

Cited by 1 | Viewed by 1251

Abstract

Autonomous navigation relies on the crucial aspect of perceiving the environment to ensure the safe navigation of an autonomous platform, taking into consideration surrounding objects and their potential movements. Consequently, a fundamental requirement arises to accurately track and predict these objects’ trajectories. Three [...] Read more.

Autonomous navigation relies on the crucial aspect of perceiving the environment to ensure the safe navigation of an autonomous platform, taking into consideration surrounding objects and their potential movements. Consequently, a fundamental requirement arises to accurately track and predict these objects’ trajectories. Three deep recurrent network architectures were defined to achieve this, fine-tuning their weights to optimize the tracking process. The effectiveness of this proposed pipeline has been assessed, with diverse tracking scenarios demonstrated in both sub-urban and highway environments. The evaluations have yielded promising results, affirming the potential of this approach in enhancing autonomous navigation capabilities. Full article

(This article belongs to the Special Issue Multi-Sensor Technology for Target Tracking, Positioning and Navigation)

► Show Figures

Figure 1

24 pages, 8459 KiB

Open AccessArticle

Robust Localization of Industrial Park UGV and Prior Map Maintenance

by Fanrui Luo, Zhenyu Liu, Fengshan Zou, Mingmin Liu, Yang Cheng and Xiaoyu Li

Sensors 2023, 23(15), 6987; https://doi.org/10.3390/s23156987 - 06 Aug 2023

Cited by 2 | Viewed by 1151

Abstract

The precise localization of unmanned ground vehicles (UGVs) in industrial parks without prior GPS measurements presents a significant challenge. Simultaneous localization and mapping (SLAM) techniques can address this challenge by capturing environmental features, using sensors for real-time UGV localization. In order to increase [...] Read more.

The precise localization of unmanned ground vehicles (UGVs) in industrial parks without prior GPS measurements presents a significant challenge. Simultaneous localization and mapping (SLAM) techniques can address this challenge by capturing environmental features, using sensors for real-time UGV localization. In order to increase the real-time localization accuracy and efficiency of UGVs, and to improve the robustness of UGVs’ odometry within industrial parks—thereby addressing issues related to UGVs’ motion control discontinuity and odometry drift—this paper proposes a tightly coupled LiDAR-IMU odometry method based on FAST-LIO2, integrating ground constraints and a novel feature extraction method. Additionally, a novel maintenance method of prior maps is proposed. The front-end module acquires the prior pose of the UGV by combining the detection and correction of relocation with point cloud registration. Then, the proposed maintenance method of prior maps is used to hierarchically and partitionally segregate and perform the real-time maintenance of the prior maps. At the back-end, real-time localization is achieved by the proposed tightly coupled LiDAR-IMU odometry that incorporates ground constraints. Furthermore, a feature extraction method based on the bidirectional-projection plane slope difference filter is proposed, enabling efficient and accurate point cloud feature extraction for edge, planar and ground points. Finally, the proposed method is evaluated, using self-collected datasets from industrial parks and the KITTI dataset. Our experimental results demonstrate that, compared to FAST-LIO2 and FAST-LIO2 with the curvature feature extraction method, the proposed method improved the odometry accuracy by 30.19% and 48.24% on the KITTI dataset. The efficiency of odometry was improved by 56.72% and 40.06%. When leveraging prior maps, the UGV achieved centimeter-level localization accuracy. The localization accuracy of the proposed method was improved by 46.367% compared to FAST-LIO2 on self-collected datasets, and the located efficiency was improved by 32.33%. The z-axis-located accuracy of the proposed method reached millimeter-level accuracy. The proposed prior map maintenance method reduced RAM usage by 64% compared to traditional methods. Full article

(This article belongs to the Special Issue Multi-Sensor Technology for Target Tracking, Positioning and Navigation)

► Show Figures

Figure 1

19 pages, 6558 KiB

Open AccessArticle

Learning Modality Complementary Features with Mixed Attention Mechanism for RGB-T Tracking

by Yang Luo, Xiqing Guo, Mingtao Dong and Jin Yu

Sensors 2023, 23(14), 6609; https://doi.org/10.3390/s23146609 - 22 Jul 2023

Cited by 2 | Viewed by 931

Abstract

RGB-T tracking involves the use of images from both visible and thermal modalities. The primary objective is to adaptively leverage the relatively dominant modality in varying conditions to achieve more robust tracking compared to single-modality tracking. An RGB-T tracker based on a mixed-attention [...] Read more.

RGB-T tracking involves the use of images from both visible and thermal modalities. The primary objective is to adaptively leverage the relatively dominant modality in varying conditions to achieve more robust tracking compared to single-modality tracking. An RGB-T tracker based on a mixed-attention mechanism to achieve a complementary fusion of modalities (referred to as MACFT) is proposed in this paper. In the feature extraction stage, we utilize different transformer backbone branches to extract specific and shared information from different modalities. By performing mixed-attention operations in the backbone to enable information interaction and self-enhancement between the template and search images, a robust feature representation is constructed that better understands the high-level semantic features of the target. Then, in the feature fusion stage, a modality shared-specific feature interaction structure was designed based on a mixed-attention mechanism, effectively suppressing low-quality modality noise while enhancing the information from the dominant modality. Evaluation on multiple RGB-T public datasets demonstrates that our proposed tracker outperforms other RGB-T trackers on general evaluation metrics while also being able to adapt to long-term tracking scenarios. Full article

(This article belongs to the Special Issue Multi-Sensor Technology for Target Tracking, Positioning and Navigation)

► Show Figures

Figure 1

13 pages, 4001 KiB

Open AccessArticle

An Improved In-Motion Coarse Alignment Method for SINS/GPS Integration with Initial Velocity Error Suppression

by Yukun Wang, Xiuli Ning and Xiang Xu

Sensors 2023, 23(7), 3662; https://doi.org/10.3390/s23073662 - 31 Mar 2023

Cited by 1 | Viewed by 952

Abstract

The integrated system with the strapdown inertial navigation system (SINS) and the global positioning system (GPS) is the most popular navigation mode. It has been used in many navigation fields. Before the integrated system works properly, it must determine the initial attitude for [...] Read more.

The integrated system with the strapdown inertial navigation system (SINS) and the global positioning system (GPS) is the most popular navigation mode. It has been used in many navigation fields. Before the integrated system works properly, it must determine the initial attitude for SINS. In SINS/GPS-integrated systems, the navigational velocity can be used to carry out the initial alignment when the system is installed in the in-motion vehicle. However, the initial velocity errors are not considered in the current popular in-motion alignment methods for SINS/GPS integration. It is well-known that the initial velocity errors must exist when the initial velocity is obtained from the GPS outputs. In this paper, an improved method was proposed to solve this problem. By analyzing the original observation vectors in the in-motion coarse alignment method, an average operation was used to construct the intermediate vectors, and the new observation vector can be calculated by subtracting the intermediate vector from the original observation vector. Then, the initial velocity errors can be eliminated from the newly constructed observation vector. Thus, the interferences of the initial velocity errors for the initial alignment process can be suppressed. The simulation and field tests are designed to verify the performance of the proposed method. The tests results showed that the proposed method can obtain the higher accurate results than the current methods when the initial velocity is considered. Additionally, the results of the proposed method were similar to the current methods when the initial velocity errors were not considered. This shows that the initial velocity errors were eliminated effectively by the proposed method, and the alignment accuracy were not decreased. Full article

(This article belongs to the Special Issue Multi-Sensor Technology for Target Tracking, Positioning and Navigation)

► Show Figures

Figure 1

17 pages, 10734 KiB

Open AccessArticle

Multi-Scale Feature Interactive Fusion Network for RGBT Tracking

by Xianbing Xiao, Xingzhong Xiong, Fanqin Meng and Zhen Chen

Sensors 2023, 23(7), 3410; https://doi.org/10.3390/s23073410 - 24 Mar 2023

Cited by 2 | Viewed by 1986

Abstract

The fusion tracking of RGB and thermal infrared image (RGBT) is paid wide attention to due to their complementary advantages. Currently, most algorithms obtain modality weights through attention mechanisms to integrate multi-modalities information. They do not fully exploit the multi-scale information and ignore [...] Read more.

The fusion tracking of RGB and thermal infrared image (RGBT) is paid wide attention to due to their complementary advantages. Currently, most algorithms obtain modality weights through attention mechanisms to integrate multi-modalities information. They do not fully exploit the multi-scale information and ignore the rich contextual information among features, which limits the tracking performance to some extent. To solve this problem, this work proposes a new multi-scale feature interactive fusion network (MSIFNet) for RGBT tracking. Specifically, we use different convolution branches for multi-scale feature extraction and aggregate them through the feature selection module adaptively. At the same time, a Transformer interactive fusion module is proposed to build long-distance dependencies and enhance semantic representation further. Finally, a global feature fusion module is designed to adjust the global information adaptively. Numerous experiments on publicly available GTOT, RGBT234, and LasHeR datasets show that our algorithm outperforms the current mainstream tracking algorithms. Full article

(This article belongs to the Special Issue Multi-Sensor Technology for Target Tracking, Positioning and Navigation)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Multi-Sensor Technology for Target Tracking, Positioning and Navigation

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Published Papers (9 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI