RBPF-MSIS: Toward Rao-Blackwellized Particle Filter SLAM for Autonomous Underwater Vehicle With Slow Mechanical Scanning Imaging Sonar

Chen, Ling; Yang, An-Shik; Hu, Huosheng; Naeem, Wasif

doi:10.1109/jsyst.2019.2938599

Cited by 24 publications

(11 citation statements)

References 20 publications

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“…For example, iSAM [125] updates a matrix factorization to solve the offline SLAM and HOGMAN [126] optimizes the calculation of the spatial distribution over a manifold. Particle filters [127] and artificial intelligence (AI) [128] can be used in both types of SLAM. In particle filter-based SLAM, the pose and all features are represented by particles in the state space [129].…”

Section: B Inter-navigationmentioning

confidence: 99%

Reliable Data Collection Techniques in Underwater Wireless Sensor Networks: A Survey

Wei

Guo

Wang

et al. 2022

IEEE Commun. Surv. Tutorials

104

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Reliable data collection techniques, whose aim is to ensure that sensed data are received successfully by a sink, are essential for applications in Underwater Wireless Sensor Networks (UWSNs). However, traditional data collection with Radio Frequency (RF) functions poorly in UWSNs due to peculiar features of underwater. Moreover, acoustic communication creates challenges for the reliability of data collection such as high bit error rate, packet collision and voids in routing. Furthermore, the deployment of Autonomous Underwater Vehicles (AUVs) in some scenarios changed the paradigm of data collection and introduced new issues that affect reliability such as inaccurate navigation and lengthy travel time. Consequently, numerous studies focus on the relative reliability of various currently available data collection in UWSNs. In this paper, we first review the problems specific to UWSNs and their impact on reliable data collection. It is followed by a discussion about characteristics, challenges, and features associated with the design of reliable techniques in UWSNs. Afterward, to provide readers with an overview of reliable data collection techniques in UWSNs, this paper categorizes them according to their ability to enhance reliability at all the key stages of data collection. In this categorization framework, the advantages and disadvantages of each technique have been in-depth discussed. Finally, several possible areas for further research are identified and discussed.

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Section: B Inter-navigationmentioning

confidence: 99%

Reliable Data Collection Techniques in Underwater Wireless Sensor Networks: A Survey

Wei

Guo

Wang

et al. 2022

IEEE Commun. Surv. Tutorials

104

View full text Add to dashboard Cite

show abstract

“…Chen et al solved the problem of particle degradation by adding resampling process. The basic idea of resampling is to copy the particles with high weights in the resampling stage to achieve the purpose of suppressing the increase of the number of particles with low weights [ 15 ]. A new PF algorithm of particle swarm optimization simulated annealing proposed by Zhu et al overcomes the difficulty of sampling in high-dimensional space [ 16 ] Algorithm 1 .…”

Section: Related Workmentioning

confidence: 99%

Research on SLAM Road Sign Observation Based on Particle Filter

Wang

2022

Computational Intelligence and Neuroscience

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With the development of computer hardware technology, the real-time problem of visual target tracking algorithm increasingly depends on hardware solutions. The core problem of visual target tracking is how to enhance the robustness of tracking algorithm to various complex background environments and various interference factors. Aiming at overcoming the defect that the traditional SLAM (simultaneous localization and map building) algorithm based on EKF (extended Kalman filter) has a slow repair speed for environmental interference, a Monocular SLAM_WOCPF (Monocular vision SLAM based on weight optimization combined particle filter) algorithm is proposed. The weights of all particles are reoptimized in the particle set and they are combined with the tendency of particles to degenerate and deplete. In this way, the chance of self replication of low weight particles is increased, thus increasing the diversity of the whole sample. Furthermore, the improved PF (particle filter) algorithm is applied to solve the problem of road sign observation of mobile robots, so as to expand its application scope. The results show that the mean road sign errors of the Monocular SLAM_WOCPF algorithm in two noise environments are 0.332/m and 0.441/m. The conclusion shows that the Monocular SLAM_WOCPF road sign observation method proposed in this paper can effectively improve the matching success rate of visual road signs and improve the observation quality.

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“…Obviously we cannot make the same assumption in our context. Chen et al [17] also recently proposed a Rao-Blackwellized particle filter (RBPF) SLAM framework using MSIS for the same application. They considered a beam enpoint model to compute the likelihood of an observation which is classical in the occupancy grid approach.…”

Section: Related Workmentioning

confidence: 99%

Elevation Angle Estimations of Wide-Beam Acoustic Sonar Measurements for Autonomous Underwater Karst Exploration

Breux

Lapierre

2020

Sensors

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This paper proposes a solution for merging the measurements from two perpendicular profiling sonars with different beam-widths, in the context of underwater karst (cave) exploration and mapping. This work is a key step towards the development of a full 6D pose SLAM framework adapted to karst aquifer, where potential water turbidity disqualifies vision-based methods, hence relying on acoustic sonar measurements. Those environments have complex geometries which require 3D sensing. Wide-beam sonars are mandatory to cover previously seen surfaces but do not provide 3D measurements as the elevation angles are unknown. The approach proposed in this paper leverages the narrow-beam sonar measurements to estimate local karst surface with Gaussian process regression. The estimated surface is then further exploited to infer scaled-beta distributions of elevation angles from a wide-beam sonar. The pertinence of the method was validated through experiments on simulated environments. As a result, this approach allows one to benefit from the high coverage provided by wide-beam sonars without the drawback of loosing 3D information.

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RBPF-MSIS: Toward Rao-Blackwellized Particle Filter SLAM for Autonomous Underwater Vehicle With Slow Mechanical Scanning Imaging Sonar

Cited by 24 publications

References 20 publications

Reliable Data Collection Techniques in Underwater Wireless Sensor Networks: A Survey

Reliable Data Collection Techniques in Underwater Wireless Sensor Networks: A Survey

Research on SLAM Road Sign Observation Based on Particle Filter

Elevation Angle Estimations of Wide-Beam Acoustic Sonar Measurements for Autonomous Underwater Karst Exploration

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