Large-area visually augmented navigation for autonomous underwater vehicles

Abstract

This thesis describes a vision-based, large-area, simultaneous localization and mapping (SLAM) algorithm that respects the low-overlap imagery constraints typical of autonomous underwater vehicles (AUVs) while exploiting the inertial sensor information that is routinely available on such platforms. We adopt a systems-level approach exploiting the complementary aspects of inertial sensing and visual perception from a calibrated pose-instrumented platform. This systems-level strategy yields a robust solution to underwater imaging that overcomes many of the unique challenges of a marine environment (e.g., unstructured terrain, low-overlap imagery, moving light source).Our large-area SLAM algorithm recursively incorporates relative-pose constraints using a view-based representation that exploits exact sparsity in the Gaussian canonical form. This sparsity allows for efficient O(n) update complexity in the number of images composing the view-based map by utilizing recent multilevel relaxation techniques. We show that our algorithmic formulation is inherently sparse unlike other feature-based canonical SLAM algorithms, which impose sparseness via pruning approximations. In particular, we investigate the sparsification methodology employed by sparse extended information filters (SEIFs) and offer new insight as to why, and how, its approximation can lead to inconsistencies in the estimated state errors. Lastly, we present a novel algorithm for efficiently extracting consistent marginal covariances useful for data association from the information matrix.In summary, this thesis advances the current state-of-the-art in underwater visual navigation by demonstrating end-to-end automatic processing of the largest visually navigated dataset to date using data collected from a survey of the RMS Titanic (path length over 3 km and 3100 m 2 of mapped area). This accomplishment embodies the summed contributions of this thesis to several current SLAM research issues including scalability, 6 degree of freedom motion, unstructured environments, and visual perception.

Adaptive autonomous underwater vehicles for littoral surveillance

“…Potential other applications for AUVs in marine robotics include oceanographic measurements, pipeline inspection, and sea-floor mapping. Research into all these areas is also growing and includes subjects such as path planning [27,34,39], cooperative localization [2,18,41,42], and simultaneous localization and mapping (SLAM) [5,17,25,33,40].…”

Visually Mapping the RMS Titanic: Conservative Covariance Estimates for SLAM Information Filters

“…Figure 6 depicts a block-diagram of the overall pairwise image processing pipeline. For further details on our systemslevel image processing, including link hypothesis and twoview pose-constrained correspondence searches, the reader is referred to Eustice et al (2004Eustice et al ( , 2006a, Eustice (2005), Pizarro et al (2003Pizarro et al ( , 2004.…”

Navigation positioning algorithm for underwater gliders in three-dimensional space

“…Although the SLAM algorithm based on the EKF method (EKF-SLAM) has been widely applied to AUVs navigation positioning [11][12][13][14][15][16], gliders are quite different from conventional AUVs. The glider [3,17] has neither cameras or imaging sonar which can identify environmental information, nor an inertial navigation system (INS) which can realize the simultaneous localization of the glider and environment characteristics.…”