107 research outputs found
AUV SLAM and experiments using a mechanical scanning forward-looking sonar
Navigation technology is one of the most important challenges in the applications of autonomous underwater vehicles (AUVs) which navigate in the complex undersea environment. The ability of localizing a robot and accurately mapping its surroundings simultaneously, namely the simultaneous localization and mapping (SLAM) problem, is a key prerequisite of truly autonomous robots. In this paper, a modified-FastSLAM algorithm is proposed and used in the navigation for our C-Ranger research platform, an open-frame AUV. A mechanical scanning imaging sonar is chosen as the active sensor for the AUV. The modified-FastSLAM implements the update relying on the on-board sensors of C-Ranger. On the other hand, the algorithm employs the data association which combines the single particle maximum likelihood method with modified negative evidence method, and uses the rank-based resampling to overcome the particle depletion problem. In order to verify the feasibility of the proposed methods, both simulation experiments and sea trials for C-Ranger are conducted. The experimental results show the modified-FastSLAM employed for the navigation of the C-Ranger AUV is much more effective and accurate compared with the traditional methods
Archaeology via underwater robots : mapping and localization within Maltese cistern systems
This paper documents the application of several
underwater robot mapping and localization techniques used
during an archaeological expedition. The goal of this project was
to explore and map ancient cisterns located on the islands of
Malta and Gozo. The cisterns of interest acted as water storage
systems for fortresses, private homes, and churches. They often
consisted of several connected chambers, still containing water. A
sonar-equipped Remotely Operated Vehicle (ROV) was deployed
into these cisterns to obtain both video footage and sonar range
measurements. Four different mapping and localization
techniques were employed including 1) Sonar image mosaics
using stationary sonar scans, and 2) Simultaneous Localization
and Mapping (SLAM) while the vehicle was in motion, 3) SLAM
using stationary sonar scans, and 4) Localization using previously
created maps. Two dimensional maps of 6 different cisterns were
successfully constructed. It is estimated that the cisterns were
built as far back as 300 B.C.peer-reviewe
CES-515 Towards Localization and Mapping of Autonomous Underwater Vehicles: A Survey
Autonomous Underwater Vehicles (AUVs) have been used for a huge number of tasks ranging from commercial, military and research areas etc, while the fundamental function of a successful AUV is its localization and mapping ability. This report aims to review the relevant elements of localization and mapping for AUVs. First, a brief introduction of the concept and the historical development of AUVs is given; then a relatively detailed description of the sensor system used for AUV navigation is provided. As the main part of the report, a comprehensive investigation of the simultaneous localization and mapping (SLAM) for AUVs are conducted, including its application examples. Finally a brief conclusion is summarized
The Malta cistern mapping project : expedition II
This paper documents the second of two
archeological expeditions that employed several
underwater robot mapping and localization
techniques. The goal of this project is to explore
and map ancient cisterns located on the islands of
Malta and Gozo. Dating back to 300 B.C., the
cisterns of interest acted as water storage systems
for fortresses, private homes, and churches. They
often consisted of several connected chambers,
still containing water. A Remotely Operated
Vehicle (ROV), was deployed into cisterns to
obtain video and sonar images. Using a variety of
sonar based mapping techniques, two-dimensional
maps of 18 different cisterns were created.peer-reviewe
Mapping and visualizing ancient water storage systems with an ROV - an approach based on fusing stationary scans within a particle filter
This paper presents a new method for construct-
ing 2D maps of enclosed underwater structures using an
underwater robot equipped with only a 2D scanning sonar,
compass and depth sensor. In particular, no motion model or
odometry is used. To accomplish this, a two step offline SLAM
method is applied to a set of stationary sonar scans. In the
first step, the change in position of the robot between each
consecutive pair of stationary sonar scans is estimated using
a particle filter. This set of pair wise relative scan positions
is used to create an estimate of each scan’s position within
a global coordinate frame using a weighted least squares fit
that optimizes consistency between the relative positions of the
entire set of scans. In the second step of the method, scans and
their estimated positions act as inputs to a mapping algorithm
that constructs 2D octree-based evidence grid maps of the site.
This work is motivated by a multi-year archeological project
that aims to construct maps of ancient water storage systems,
i.e. cisterns, on the islands of Malta and Gozo. Cisterns, wells,
and water galleries within fortresses, churches and homes oper-
ated as water storage systems as far back as 2000 B.C. Using a
Remotely Operated Vehicle (ROV) these water storage systems
located around the islands were explored while collecting video,
still images, sonar, depth, and compass measurements. Data
gathered from 3 different expeditions has produced maps of
over 60 sites. Presented are results from applying the new
mapping method to both a swimming pool of known size and
to several of the previously unexplored water storage systems.peer-reviewe
Towards three-dimensional underwater mapping without odometry
This paper presents a method for the creation of three-dimensional maps of underwater cisterns and wells using a submersible robot equipped with two scanning sonars and a compass. Previous work in this area utilized a particle filter to perform offline simultaneous localization and mapping (SLAM) in two dimensions using a single sonar [11]. This work utilizes scan matching and incorporates an additional sonar that scans in a perpendicular plane. Given a set of overlapping horizontal and vertical sonar scans, an algorithm was implemented to map underwater chambers by matching sets of scans using a weighted iterative closest point (ICP) method. This matching process has been augmented to align the features of the underwater cistern data without robot odometry. Results from a swimming pool and an archeological site trials indicate successful mapping is achieved
A review: Simultaneous localization and mapping algorithms
Simultaneous Localization and Mapping (SLAM) involves creating an environmental map based on sensor data, while concurrently keeping track of the robot’s current position. Efficient and accurate SLAM is crucial for any mobile robot to perform robust navigation. It is also the keystone for higher-level tasks such as path planning and autonomous navigation. The past two decades have seen rapid and exciting progress in solving the SLAM problem together with many compelling implementations of SLAM methods. In this paper, we will review the two common families of SLAM algorithms: Kalman filter with its variations and particle filters. This article complements other surveys in this ?eld by reviewing the representative algorithms and the state-of-the-art in each family. It clearly identifies the inherent relationship between the state estimation via the KF versus PF techniques, all of which are derivations of Bayes rule
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