Point Features Extraction: Towards Slam for an Autonomous Underwater Vehicle (original) (raw)

Feature extraction for underwater visual SLAM

2011

Detecting and selecting proper landmarks is a key issue to solve Simultaneous Localization and Mapping (SLAM). In this work, we present a novel approach to perform this landmark detection. Our approach is based on using three sources of information: 1) three-dimensional topological information from SLAM; 2) context information to characterize regions of interest (RoI); and 3) features extracted from these RoIs. Topological information is taken from the SLAM algorithm, i.e. the three-dimensional approximate position of the landmark with a certain level of uncertainty. Contextual information is obtained by segmenting the image into background and RoIs. Features extracted from points of interest are then computed by using common feature extractors such as SIFT and SURF. This information is used to associate new observations with known landmarks obtained from previous observations. The proposed approach is tested under a real unstructured underwater environment using the SPARUS AUV. Results demonstrate the validity of our approach, improving map consistency.

A trajectory based framework to perform underwater SLAM using imaging sonar scans

2010

This paper proposes a framework to perform Simultaneous Localization and Mapping (SLAM) using the scans gathered by a Mechanically Scanned Imaging Sonar (MSIS). To this end, the acoustic profiles provided by the MSIS are processed to obtain range data. Also, dead reckoning is used to compensate the robot motion during the sonar mechanical scanning and build range scans.

Appearance-based odometry and mapping with feature descriptors for underwater robots

Journal of The Brazilian Computer Society, 2009

The use of Autonomous Underwater Vehicles (AUVs) for underwater tasks is a promising robotic field. These robots can carry visual inspection cameras. Besides serving the activities of inspection and mapping, the captured images can also be used to aid navigation and localization of the robots. Visual odometry is the process of determining the position and orientation of a robot by analyzing the associated camera images. It has been used in a wide variety of non-standard locomotion robotic methods. In this context, this paper proposes an approach to visual odometry and mapping of underwater vehicles. Supposing the use of inspection cameras, this proposal is composed of two stages: i) the use of computer vision for visual odometry, extracting landmarks in underwater image sequences and ii) the development of topological maps for localization and navigation. The integration of such systems will allow visual odometry, localization and mapping of the environment. A set of tests with real robots was accomplished, regarding online and performance issues. The results reveals an accuracy and robust approach to several underwater conditions, as illumination and noise, leading to a promissory and original visual odometry and mapping technique.

NLMAP - visual-based self localization and mapping for Autonomous Underwater Vehicles

OCEANS 2008, 2008

The use of Autonomous Underwater Vehicles (AUVs) for visual inspection tasks is a promising robotic field. The images captured by the robots can also aid in their localization/navigation. In this context, this paper proposes an approach to localization and mapping problem of underwater vehicle. Supposing the use of inspection cameras, this proposal is composed of two stages: i the use of computer vision through the algorithm SIFT to extract the features in underwater image sequences and ii the development of topological maps to localization and navigation. The integration of such systems will permit simultaneous localization and mapping of the environment. A set of tests with real robots was accomplished, regarding online and performance issues. The results reveals an accuracy and robust approach to several bottom conditions, illumination and noise, leading to a promissory and original SLAM technique.

Pose-based SLAM with probabilistic scan matching algorithm using a mechanical scanned imaging sonar

2009

This paper proposes a pose-based algorithm to solve the full SLAM problem for an autonomous underwater vehicle (AUV), navigating in an unknown and possibly unstructured environment. The technique incorporate probabilistic scan matching with range scans gathered from a mechanical scanning imaging sonar (MSIS) and the robot dead-reckoning displacements estimated from a Doppler velocity log (DVL) and a motion reference unit (MRU). The proposed method utilizes two extended Kalman filters (EKF). The first, estimates the local path travelled by the robot while grabbing the scan as well as its uncertainty and provides position estimates for correcting the distortions that the vehicle motion produces in the acoustic images. The second is an augment state EKF that estimates and keeps the registered scans poses. The raw data from the sensors are processed and fused in-line. No priory structural information or initial pose are considered. The algorithm has been tested on an AUV guided along a 600 m path within a marina environment, showing the viability of the proposed approach.

Underwater SLAM: Challenges, state of the art, algorithms and a new biologically-inspired approach

5th IEEE RAS/EMBS International Conference on Biomedical Robotics and Biomechatronics, 2014

The unstructured scenario, the extraction of significant features, the imprecision of sensors along with the impossibility of using GPS signals are some of the challenges encountered in underwater environments. Given this adverse context, the Simultaneous Localization and Mapping techniques (SLAM) attempt to localize the robot in an efficient way in an unknown underwater environment while, at the same time, generate a representative model of the environment. In this paper, we focus on key topics related to SLAM applications in underwater environments. Moreover, a review of major studies in the literature and proposed solutions for addressing the problem are presented. Given the limitations of probabilistic approaches, a new alternative based on a bio-inspired model is highlighted.