Learning Type-2 Fuzzy Logic for Factor Graph Based-Robust Pose Estimation With Multi-Sensor Fusion

Nam, Dinh Van; Kim, Gon–Woo

doi:10.1109/tits.2023.3234595

Cited by 11 publications

(5 citation statements)

References 51 publications

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“…There are also studies proposing factor graph-based estimation for learning tactile models and ground encoding using CNN [42,43]. In order to simplify the software architecture and reduce computational requirements, a novel learnable observation noise model for robust pose estimation by integrating type-2 TS FIS and factor graph optimization was proposed [44], which uses a visual LiDAR inertial sensor. However, these machine learning methods still require high-performance computing units to provide substantial computational support during both training and real-time calculation processes, which is not the original intention of this study.…”

Section: Low-cost 3d Environment Reconstruction Systemmentioning

confidence: 99%

Indoor Environment Reconstruction for Unmanned System Using Multiple Low-Cost Sensors

Wang,

Ding,

Wang

et al. 2024

Remote Sensing

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Considering that laser scan stations are expensive and heavy for the indoor environment reconstruction of unmanned systems, a low-cost and light reconstruction system is proposed in this research. The system consists of a cross-structured light visual (SLV) sensor and an inertial navigation system (INS). The cross-SLV sensor is used to scan the surroundings and to estimate the angle change between two adjacent measurements. To improve the robustness and accuracy of the angle measurement, a Kalman Filter (KF) with simple construction is designed to fuse the inertial information from the INS. The factors which influence ranging accuracy are analyzed and ranging experiments show that the SLV sensor has an accuracy of higher than 2% when the distance is less than 4 m. Then the reconstruction results of a kitchen and corridor show that the error of most points is less than 50 mm for either kitchen (94%) or corridor (85%), and the mean errors and standard deviations of kitchen and corridor are less than 20 mm and 30 mm, respectively.

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Section: Low-cost 3d Environment Reconstruction Systemmentioning

confidence: 99%

Indoor Environment Reconstruction for Unmanned System Using Multiple Low-Cost Sensors

Wang,

Ding,

Wang

et al. 2024

Remote Sensing

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“…The common means of state estimation for mobile robots include the global navigation satellite system (GNSS), landmarks, LIDAR, cameras, encoders, and inertial measurement units (IMU) [4][5][6][7]. These techniques are susceptible to environmental constraints, such as varying light sources for cameras and performance degradation for LiDAR in corridor environments.…”

Section: Introductionmentioning

confidence: 99%

Advancing robust state estimation of wheeled robots in degenerate environments: harnessing ground manifold and motion states

Liang,

Tao,

Zhu

et al. 2024

Meas. Sci. Technol.

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State estimation is crucial for enabling autonomous mobility in mobile robots. However, traditional localization methods often falter in degraded environments, including issues like visual occlusion, lidar performance degradation, and Global Navigation Satellite System(GNSS) signal interference. This paper presents a novel estimation approach for wheeled robots, exclusively utilizing proprioceptive sensors such as encoders and inertial measurement units (IMU). Initially, the motion manifolds extracted from the historical trajectories are used to assist the encoder in realizing the orientation estimation. Furthermore, a hybrid neural network is designed to categorize the robot's operational state, and the corresponding pseudo-constraints are added to improve the estimation accuracy. We utilize an error state Kalman filter for the encoder and IMU data fusion. Lastly, comprehensive testing is conducted using both datasets and real-world robotic platforms. The findings underscore that the integration of manifold and motion constraints within our proposed state estimator substantially elevates accuracy compared to conventional approaches. Compare with the methods commonly used in engineering, the accuracy of this method is improved by more than 20%. Crucially, this methodology enables dependable estimation even in degraded environments.

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“…Based on the above literature review, the FLS is frequently chosen as an effective tool for system regulation addressed to various negative factors; for instance, an unmodeled dynamics system. Nevertheless, the FLS is designed depending on the experience of the engineer, which is not capable of self-learning proficiency [23][24][25][26]. In recent decades, neural networks (NN) have been extensively researched in control algorithms, possessing energetic self-learning capabilities.…”

Section: Introductionmentioning

confidence: 99%

Development of an Adaptive Fuzzy-Neural Controller for Temperature Control in a Brick Tunnel Kiln

Phan,

Nguyen,

Dinh

et al. 2024

Electronics

Self Cite

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This research focuses on developing an adaptive fuzzy-neural control system to manage and maintain temperature in brick tunnel kilns. The problem is how to optimize performance and energy consumption in the brick production process. A control algorithm using a combination of a fuzzy logic system and neural network is proposed to automatically adjust temperature parameters, optimize production efficiency, and reduce energy consumption. Simulation and experimental results demonstrate the outstanding performance of the presented system, with significant improvements in energy efficiency and product quality compared to traditional control methods. Moreover, the obtained results exhibit the potential for wide application of the adaptive neuro-fuzzy control method in academic study or industrial production processes.

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Learning Type-2 Fuzzy Logic for Factor Graph Based-Robust Pose Estimation With Multi-Sensor Fusion

Cited by 11 publications

References 51 publications

Indoor Environment Reconstruction for Unmanned System Using Multiple Low-Cost Sensors

Indoor Environment Reconstruction for Unmanned System Using Multiple Low-Cost Sensors

Advancing robust state estimation of wheeled robots in degenerate environments: harnessing ground manifold and motion states

Development of an Adaptive Fuzzy-Neural Controller for Temperature Control in a Brick Tunnel Kiln

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