scholarly journals Underwater Localization and Mapping Based on Multi-Beam Forward Looking Sonar

2022 ◽  
Vol 15 ◽  
Author(s):  
Chensheng Cheng ◽  
Can Wang ◽  
Dianyu Yang ◽  
Weidong Liu ◽  
Feihu Zhang
Keyword(s):  
Point Cloud ◽  
Autonomous Underwater Vehicle ◽  
Original Data ◽  
Underwater Vehicle ◽  
Vital Role ◽  
Experimental Platform ◽  
Localization And Mapping ◽  
Underwater Localization ◽  
Constrained Filtering ◽  
Forward Looking

SLAM (Simultaneous Localization And Mapping) plays a vital role in navigation tasks of AUV (Autonomous Underwater Vehicle). However, due to a vast amount of image sonar data and some acoustic equipment's inherent high latency, it is a considerable challenge to implement real-time underwater SLAM on a small AUV. This paper presents a filter based methodology for SLAM algorithms in underwater environments. First, a multi-beam forward looking sonar (MFLS) is utilized to extract environmental features. The acquired sonar image is then converted to sparse point cloud format through threshold segmentation and distance-constrained filtering to solve the calculation explosion issue caused by a large amount of original data. Second, based on the proposed method, the DVL, IMU, and sonar data are fused, the Rao-Blackwellized particle filter (RBPF)-based SLAM method is used to estimate AUV pose and generate an occupancy grid map. To verify the proposed algorithm, the underwater vehicle is equipped as an experimental platform to conduct field tasks in both the experimental pool and wild lake, respectively. Experiments illustrate that the proposed approach achieves better performance in both state estimation and suppressing divergence.

Sparse Extended Information Filter for AUV SLAM: Insights into the Optimal Sparse Time

2013 ◽  
Vol 427-429 ◽  
pp. 1670-1673 ◽  
Author(s):  
Hao Zhang ◽  
Bo He ◽  
Ning Luan
Keyword(s):  
Autonomous Underwater Vehicle ◽  
Computation Time ◽  
Underwater Vehicle ◽  
Information Loss ◽  
Sea Trial ◽  
Time Consumption ◽  
Localization And Mapping ◽  
Information Filter ◽  
Slam Algorithm ◽  
And Storage

Sparse extended information filter-based simultaneous localization and mapping (SEIF-based SLAM) algorithm can reflect significant advantages in terms of computation time and storage memories. However, SEIF-SLAM is easily prone to overconfidence due to sparsification strategy. In this paper we will consider the time consumption and information loss of sparse operation, and get the optimal sparse time. In order to verify the feasibility of sparsification, a sea trial for autonomous underwater vehicle (AUV) C-Ranger was conducted in Tuandao Bay. The experimental results will show the improved algorithm is much more effective and accurate comparedwithothermethods.

scholarly journals An Overview of Autonomous Underwater Vehicle Research and Testbed at PeRL

2009 ◽  
Vol 43 (2) ◽  
pp. 33-47 ◽  
Author(s):  
Hunter C. Brown ◽  
Ayoung Kim ◽  
Ryan M. Eustice
Keyword(s):  
Real Time ◽  
Autonomous Underwater Vehicle ◽  
Underwater Vehicle ◽  
University Of Michigan ◽  
Major Research ◽  
Research Areas ◽  
Localization And Mapping ◽  
Navigation And Control ◽  
The University ◽  
And Control

AbstractThis article provides a general overview of the autonomous underwater vehicle (AUV) research thrusts being pursued within the Perceptual Robotics Laboratory (PeRL) at the University of Michigan. Founded in 2007, PeRL's research centers on improving AUV autonomy via algorithmic advancements in environmentally based perceptual feedback for real-time mapping, navigation, and control. Our three major research areas are (1) real-time visual simultaneous localization and mapping (SLAM), (2) cooperative multi-vehicle navigation, and (3) perception-driven control. Pursuant to these research objectives, PeRL has developed a new multi-AUV SLAM testbed based upon a modified Ocean-Server Iver2 AUV platform. PeRL upgraded the vehicles with additional navigation and perceptual sensors for underwater SLAM research. In this article, we detail our testbed development, provide an overview of our major research thrusts, and put into context how our modified AUV testbed enables experimental real-world validation of these algorithms.

scholarly journals Visual navigation of an autonomous underwater vehicle based on the global search of image correspondences

2018 ◽  
Vol 42 (3) ◽  
pp. 457-467 ◽  
Author(s):  
A. N. Kamaev ◽  
D. A. Karmanov
Keyword(s):  
Navigation System ◽  
Point Cloud ◽  
Autonomous Underwater Vehicle ◽  
Synthetic Data ◽  
Real Data ◽  
Point Clouds ◽  
Underwater Vehicle ◽  
Search Tree ◽  
Visual Navigation ◽  
Global Search

A task of autonomous underwater vehicle (AUV) navigation is considered in the paper. The images obtained from an onboard stereo camera are used to build point clouds attached to a particular AUV position. Quantized SIFT descriptors of points are stored in a metric tree to organize an effective search procedure using a best bin first approach. Correspondences for a new point cloud are searched in a compact group of point clouds that have the largest number of similar descriptors stored in the tree. The new point cloud can be positioned relative to the other clouds without any prior information about the AUV position and uncertainty of this position. This approach increases the reliability of the AUV navigation system and makes it insensitive to data losses, textureless seafloor regions and long passes without trajectory intersections. Several algorithms are described in the paper: an algorithm of point clouds computation, an algorithm for establishing point clouds correspondence, and an algorithm of building groups of potentially linked point clouds to speedup the global search of correspondences. The general navigation algorithm consisting of three parallel subroutines: image adding, search tree updating, and global optimization is also presented. The proposed navigation system is tested on real and synthetic data. Tests on real data showed that the trajectory can be built even for an image sequence with 60% data losses with successive images that have either small or zero overlap. Tests on synthetic data showed that the constructed trajectory is close to the true one even for long missions. The average speed of image processing by the proposed navigation system is about 3 frames per second with  a middle-price desktop CPU.

Obstacle Avoidance Considerations for the REMUS Autonomous Underwater Vehicle

2003 ◽  
Author(s):  
Lynn R. Fodrea ◽  
Anthony J. Healey
Keyword(s):  
Obstacle Avoidance ◽  
Autonomous Underwater Vehicle ◽  
Autonomous Underwater Vehicles ◽  
Underwater Vehicle ◽  
Gain Factor ◽  
Obstacle Detection ◽  
Dynamic Obstacle Avoidance ◽  
Dynamic Obstacle ◽  
World Mission ◽  
Forward Looking

Future Naval operations necessitate the incorporation of autonomous underwater vehicles into a collaborative network. In future complex missions, a forward look capability will also be required to map and avoid obstacles such as sunken ships. This work examines obstacle avoidance behaviors using a hypothetical forward-looking sonar for the autonomous underwater vehicle REMUS. Hydrodynamic coefficients are used to develop steering equations that model REMUS through a track of specified points similar to a real-world mission track. A two-dimensional forward-looking sonar model with a 120° horizontal scan and a 110 meter radial range is modeled for obstacle detection. Sonar mappings from geographic range-bearing coordinates are developed for implementation in MATLAB simulations. The product of bearing and range weighting functions form the gain factor for a dynamic obstacle avoidance behavior. The overall vehicle heading error incorporates this obstacle avoidance term to develop a path around detected objects. REMUS is a highly responsive vehicle in the model and is capable of avoiding multiple objects in proximity along its track path.

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