AQUALOC: An underwater dataset for visual–inertial–pressure localization

Abstract

We present a new dataset, dedicated to the development of simultaneous localization and mapping methods for underwater vehicles navigating close to the seabed. The data sequences composing this dataset are recorded in three different environments: a harbor at a depth of a few meters, a first archaeological site at a depth of 270 meters and a second site at a depth of 380 meters. The data acquisition is performed using Remotely Operated Vehicles equipped with a monocular monochromatic camera, a low-cost inertial measurement unit, a pressure sensor and a computing unit, all embedded in a single enclosure. The sensors' measurements are recorded synchronously on the computing unit and seventeen sequences have been created from all the acquired data. These sequences are made available in the form of ROS bags and as raw data. For each sequence, a trajectory has also been computed offline using a Structure-from-Motion library in order to allow the comparison with real-time localization methods. With the release of this dataset, we wish to provide data difficult to acquire and to encourage the development of vision-based localization methods dedicated to the underwater environment. The dataset can be downloaded from: http://www.lirmm.fr/aqualoc/

Saliency Improvement in Feature-Poor Surgical Environments Using Local Laplacian of Specified Histograms

“…To evaluate the image enhancement approaches, we visually analyse the processed images and choose key-point-feature based matching to validate their performance in the context of visual navigation. Multiple sets of arthroscopic images and the underwater seabed inspection Aqualoc [7], [8] are used in the evaluation.…”

“…Based on the observations from the enhancement methods we also implement the CLAHS and LLAP techniques on arthroscopic sequences for comparison. Furthermore, we evaluate the three enhancement techniques on an open source underwater Aqualoc dataset [7], [8] for completeness. The Aqualoc dataset has been deployed only in [31] where it was used to validate a positioning solution based on monocular visual odometry.…”

“…We evaluated our approach on two cadaveric datasets from knee arthroscopy and on images from an open source underwater seabed inspection dataset (Aqualoc) [7], [8] , with each of them containing 1300-1500 images. The first arthroscopic dataset (dataset 1) was acquired with a Stryker arthroscope and represents a challenging and realistic scenario complete with occlusions (tissue, water bubbles or surgical tools) and feature-poor images (see Remark 1).…”

“…Content may change prior to final publication. FIGURE 3: Matching Performance of SURF and SIFT key-point features on underwater harbour seabed inspection dataset [7], [8]. Each column represents the original and enhanced pair of images (CLAHS, LLAP, LLSH).…”

Autonomous Underwater Vehicles: Localization, Navigation, and Communication for Collaborative Missions

“…Moreover, it is hard for researches to find the proper conditions to test their visual-based and visual-SLAM algorithms in real underwater conditions. To deal with that, some datasets are being collected such as the AQUALOC dataset [97], which is dedicated to the development of SLAM methods for underwater vehicles navigating close to the seabed. The Autonomous Field Robotics Laboratory (AFRB) [98] has some datasets available for the same purpose.…”