Hanumant Singh

Exactly Sparse Delayed-State Filters for View-Based SLAM

This paper reports the novel insight that the simultaneous localization and mapping (SLAM) information matrix is exactly sparse in a delayed-state framework. Such a framework is used in view-based representations of the environment that rely upon scan-matching raw sensor data to obtain virtual observations of robot motion with respect to a place it has previously been. The exact sparseness of the delayed-state information matrix is in contrast to other recent feature-based SLAM information algorithms, such as sparse extended information filter or thin junction-tree filter, since these methods have to make approximations in order to force the feature-based SLAM information matrix to be sparse. The benefit of the exact sparsity of the delayed-state framework is that it allows one to take advantage of the information space parameterization without incurring any sparse approximation error. Therefore, it can produce equivalent results to the full-covariance solution. The approach is validated experimentally using monocular imagery for two datasets: a test-tank experiment with ground truth, and a remotely operated vehicle survey of the RMS Titanic

Visually Navigating the RMS Titanic with SLAM Information Filters

This paper describes a vision-based large-area simultaneous localization and mapping (SLAM) algorithm that respects the constraints of low-overlap imagery typical of underwater vehicles while exploiting the information associated with the inertial sensors that are routinely available on such platforms. We present a novel strategy for efficiently accessing and maintaining consistent covariance bounds within a SLAM information filter, greatly increasing the reliability of data association. The technique is based upon solving a sparse system of linear equations coupled with the application of constant-time Kalman updates. The method is shown to produce consistent covariance estimates suitable for robot planning and data association. Realworld results are presented for a vision-based 6 DOF SLAM implementation using data from a recent ROV survey of the wreck of the RMS Titanic.

Toward large-area mosaicing for underwater scientific applications

Severe attenuation and backscatter of light fundamentally limits our ability to image extended underwater scenes. Generating a composite view or mosaic from multiple overlapping images is usually the most practical and flexible way around this limitation. In this paper, we look at the general constraints associated with imaging from underwater vehicles for scientific applications - low overlap, nonuniform lighting, and unstructured motion

Advances in Doppler-based navigation of underwater robotic vehicles

and present a methodology for dealing with these constraints toward a solution of the problem of large-area global mosaicing. Our approach assumes that the extended scene is planar and determines the homographies for each image by estimating and compensating for radial distortion, topology estimation through feature-based pairwise image registration using a multiscale Harris interest point detector coupled with a feature descriptor based on Zernike moments, and global registration across all images based on the initial registration derived from the pairwise estimates. This approach is purely image based and does not assume that navigation data is available. We demonstrate the utility of our techniques using real data obtained using the Jason remotely operated vehicle (ROV) at an archaeological site covering hundreds of square meters.

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

New low-cost commercially available bottom-lock Doppler sonars can augment or replace the acoustic time-of-flight navigation systems commonly employed for three-dimensional underwater robot vehicle navigation. The paper first reviews conventional techniques for underwater vehicle navigation, and describes a Doppler-based navigation system developed by the authors. Second, we identify principal limitations to the bottom-track precision of Doppler based navigation systems. Third, we analyze the effect of heading-sensor errors on Doppler bottom-track precision. Experimental results compare bottom-track error resulting from a Doppler navigation using low-precision magnetic heading sensor with bottom-track error resulting from a high-precision a ring-laser gyroscope. The experiments were conducted during a field deployment in which the new robot navigation system enabled precision acoustic and optical survey as well as minimally invasive object recovery from hydrothermal vents in the Guaymas Basin, Gulf of California at 27/spl deg/N 111.5/spl deg/W, at 2000 m depth.

Visually Augmented Navigation for Autonomous Underwater Vehicles

This paper describes a vision-based, large-area, simultaneous localization and mapping (SLAM) algorithm that respects the low-overlap imagery constraints typical of underwater vehicles while exploiting the inertial sensor information that is routinely available on such platforms. We present a novel strategy for efficiently accessing and maintaining consistent covariance bounds within a SLAM information filter, thereby greatly increasing the reliability of data association. The technique is based upon solving a sparse system of linear equations coupled with the application of constant-time Kalman updates. The method is shown to produce consistent covariance estimates suitable for robot planning and data association. Real-world results are reported for a vision-based, six degree of freedom SLAM implementation using data from a recent survey of the wreck of the RMS Titanic.

Experimental Results in Synchronous-Clock One-Way-Travel-Time Acoustic Navigation for Autonomous Underwater Vehicles

As autonomous underwater vehicles (AUVs) are becoming routinely used in an exploratory context for ocean science, the goal of visually augmented navigation (VAN) is to improve the near-seafloor navigation precision of such vehicles without imposing the burden of having to deploy additional infrastructure. This is in contrast to traditional acoustic long baseline navigation techniques, which require the deployment, calibration, and eventual recovery of a transponder network. To achieve this goal, VAN is formulated within a vision-based simultaneous localization and mapping (SLAM) framework that exploits the systems-level complementary aspects of a camera and strap-down sensor suite. The result is an environmentally based navigation technique robust to the peculiarities of low-overlap underwater imagery. The method employs a view-based representation where camera-derived relative-pose measurements provide spatial constraints, which enforce trajectory consistency and also serve as a mechanism for loop closure, allowing for error growth to be independent of time for revisited imagery. This article outlines the multisensor VAN framework and demonstrates it to have compelling advantages over a purely vision-only approach by: 1) improving the robustness of low-overlap underwater image registration; 2) setting the free gauge scale; and 3) allowing for a disconnected camera-constraint topology.

Seabed AUV offers new platform for high‐resolution imaging

This paper reports recent experimental results in the development and deployment of a synchronous-clock acoustic navigation system suitable for the simultaneous navigation of multiple underwater vehicles. The goal of this work is to enable the task of navigating multiple autonomous underwater vehicles (AUVs) over length scales of O(100 km), while maintaining error tolerances commensurate with conventional long-baseline transponder-based navigation systems (i.e., O(1 m)), but without the requisite need for deploying, calibrating, and recovering seafloor anchored acoustic transponders. Our navigation system is comprised of an acoustic modem-based communication/navigation system that allows for onboard navigational data to be broadcast as a data packet by a source node, and for all passively receiving nodes to be able to decode the data packet to obtain a one-way travel time pseudo-range measurement and ephemeris data. We present results for two different field experiments using a two-node configuration consisting of a global positioning system (GPS) equipped surface ship acting as a global navigation aid to a Doppler-aided AUV. In each experiment, vehicle position was independently corroborated by other standard navigation means. Initial results for a maximum-likelihood sensor fusion framework are reported.

Towards Precision Robotic Maneuvering, Survey, and Manipulation in Unstructured Undersea Environments

A number of marine biological, geological, and archaeological applications share the need for high-resolution optical and acoustic imaging of the sea floor [Ballard et al., 2002; Greene et al., 2000; Shank et al., 2002]. In particular,there is a compelling need to conduct studies in depths beyond those considered reasonable for divers (∼50 m) down to depths at the shelf edge and continental slope (∼1000–2000 m). Some of the constraints associated with such work include the requirement to work off of small coastal vessels or fishing boats of opportunity,and the requirement for the vehicle components to be air-shippable to enable inexpensive deployments at far-flung oceanographic sites of interest.

Underwater Data Collection Using Robotic Sensor Networks

This paper reports recent advances in the precision control of underwater robotic vehicles for survey and manipulation missions. A new underwater vehicle navigation and control system employing a new commercially available 1,200 kHz doppler sonar is reported. Comparative experimental trials compare the performance of the new system to conventional 12 kHz and 300 kHz long baseline (LBL) acoustic navigation systems. The results demonstrate a hybrid system incorporating both doppler and LBL to provide superior tracking in comparison to doppler or LBL alone.