Harry Hess and sea-floor spreading

Harry Hess's hypothesis of sea-floor spreading brought together his long-standing interests in island arcs, oceanic topography, and the oceanic crust. The one unique feature of Hess's hypothesis was the origin of the oceanic crust as a hydration rind on the top of the mantle -- an idea that was not well received, even by the early converts to sea-floor spreading. Hess never changed his mind on this issue, and his stubbornness illuminates the logic of his discovery. Published and archival records show that 1) Hess became convinced the oceanic crust was a hydration rind as early as mid 1958, when he was still a fixist, 2) he devised sea-floor spreading in 1960 to reconcile the hydration-rind model with the newly discovered, high heat flow at oceanic ridge crests, and 3) Hess's new mobilist solution did the least amount of violence to his older fixist solution.

Bi-objective bundle adjustment: towards large-scale visual sea-floor mapping with a minimal sensor suite

Visual sea-floor mapping is a rapidly growing application for Autonomous Underwater Vehicles (AUVs). AUVs are well-suited to the task as they remove humans from a potentially dangerous environment, can reach depths human divers cannot, and are capable of long-term operation in adverse conditions. The output of sea-floor maps generated by AUVs has a number of applications in scientific monitoring: from classifying coral in high biological value sites to surveying sea sponges to evaluate marine environment health.

Visual sea-floor mapping from low overlap imagery using bi-objective bundle adjustment and constrained motion

In most visual mapping applications suited to Autonomous Underwater Vehicles (AUVs), stereo visual odometry (VO) is rarely utilised as a pose estimator as imagery is typically of very low framerate due to energy conservation and data storage requirements. This adversely affects the robustness of a vision-based pose estimator and its ability to generate a smooth trajectory. This paper presents a novel VO pipeline for low-overlap imagery from an AUV that utilises constrained motion and integrates magnetometer data in a bi-objective bundle adjustment stage to achieve low-drift pose estimates over large trajectories. We analyse the performance of a standard stereo VO algorithm and compare the results to the modified vo algorithm. Results are demonstrated in a virtual environment in addition to low-overlap imagery gathered from an AUV. The modified VO algorithm shows significantly improved pose accuracy and performance over trajectories of more than 300m. In addition, dense 3D meshes generated from the visual odometry pipeline are presented as a qualitative output of the solution.