Mapping Pamir: Multi-Session Visual-Inertial SLAM and 3D Reconstruction of an Underwater Shipwreck

This paper presents a framework for multi-session mapping of underwater environments utilizing an affordable action camera. The Visual-Inertial data are augmented by water depth recordings from a dive computer. SVIn2, an open-source VI-SLAM framework, is utilized to generate a trajectory and a sparse reconstruction for each session. Utilizing the keyframes extracted from SVIn2 and the estimated camera poses, a Structure-from-Motion (SfM) framework, COLMAP, is employed for global optimization and to produce a dense reconstruction of the target environment. The presence of calibration targets at fixed locations, when available, is used to estimate the coordinate transformation between different data collection sessions, thus transforming the different sessions into the same coordinate frame. The proposed pipeline is employed for the mapping of a shipwreck off the coast of Barbados. For the first time, both the exterior and the accessible interior parts of the wreck were mapped in two sessions, while a third session employed two cameras with different fields of view.

Underwater Dense Mapping with the First Compact 3D Sonar

In the past decade, the adoption of compact 3D range sensors, such as LiDARs, has driven the developments of robust state-estimation pipelines, making them a standard sensor for aerial, ground, and space autonomy. Unfortunately, poor propagation of electromagnetic waves underwater, has limited the visibility-independent sensing options of underwater state-estimation to acoustic range sensors, which provide 2D information including, at-best, spatially ambiguous information. This paper, to the best of our knowledge, is the first study examining the performance, capacity, and opportunities arising from the recent introduction of the first compact 3D sonar. Towards that purpose, we introduce calibration procedures for extracting the extrinsics between the 3D sonar and a camera and we provide a study on acoustic response in different surfaces and materials. Moreover, we provide novel mapping and SLAM pipelines tested in deployments in underwater cave systems and other geometrically and acoustically challenging underwater environments. Our assessment showcases the unique capacity of 3D sonars to capture consistent spatial information allowing for detailed reconstructions and localization in datasets expanding to hundreds of meters. At the same time it highlights remaining challenges related to acoustic propagation, as found also in other acoustic sensors. Datasets collected for our evaluations would be released and shared with the community to enable further research advancements.