Abstract—In this paper, we present an approach for modeling 3D environments based on octrees using a probabilistic occupancy estimation. Our technique is able to represent full 3D models including free and unknown areas. It is available as an open-source library to facilitate the development of 3D mapping systems. We also provide a detailed review of existing approaches to 3D modeling. Our approach was thoroughly evaluated using different real-world and simulated datasets. The results demonstrate that our approach is able to model the data probabilistically while, at the same time, keeping the memory requirement at a minimum. I.

Outdoor mobile robot motion planning and navigation is a challenging problem in artificial intelligence. The search space density and dimensionality, system dynamics and environmental interaction complexity, and the perceptual horizon limitation all contribute to the difficultly of this problem. It is hard to generate a motion plan between arbitrary boundary states that considers sophisticated vehicle dynamics and all feasible actions for nontrivial mobile robot systems. Accomplishing these goals in real time is even more challenging because of dynamic environments and updating perception information. This thesis develops effective search spaces for mobile robot trajectory generation, motion planning, and navigation in complex environments. Complex environments are defined as worlds where locally optimal motion plans are numerous and where the sensitivity of the cost function is highly dependent on state and motion model fidelity. Examples include domains where obstacles are prevalent, terrain shape is varied, and the consideration of terramechanical effects is important. Three specific contributions are accomplished. First, a model-predictive trajectory generation technique is developed that numerically linearizes and inverts general predictive motion models to determine parameterized actions that satisfy the two-point boundary value problem. Applications on a number of mobile robot platforms (including skidsteered field robots, planetary rovers with actively articulating chassis, mobile manipulators, and autonomous automobiles)

Abstract—This paper documents the application of several underwater robot mapping and localization techniques used during an archaeological expedition. The goal of this project was to explore and map ancient cisterns located on the islands of Malta and Gozo. The cisterns of interest acted as water storage systems for fortresses, private homes, and churches. They often consisted of several connected chambers, still containing water. A sonar-equipped Remotely Operated Vehicle (ROV) was deployed into these cisterns to obtain both video footage and sonar range measurements. Four different mapping and localization techniques were employed including 1) Sonar image mosaics using stationary sonar scans, and 2) Simultaneous Localization and Mapping (SLAM) while the vehicle was in motion, 3) SLAM using stationary sonar scans, and 4) Localization using previously created maps. Two dimensional maps of 6 different cisterns were successfully constructed. It is estimated that the cisterns were built as far back as 300 B.C. Keywords—ROV, sonar, mapping, SLAM, mosaic. I.

This paper documents the development of an underwater robot system enabled with several mapping and localization techniques applied to a particular archaeological expedition. The goal of the expedition was to explore and map ancient cisterns located on the islands of Malta and Gozo. The cisterns of interest acted as water storage systems for fortresses, private homes, and churches. Such cisterns often consisted of several connected chambers, still containing water. A sonar-equipped remotely operated vehicle (ROV) was deployed into these cisterns to obtain both video footage and sonar range measurements. Six different mapping and localization techniques were employed, including (1) sonar image mosaics using stationary sonar scans, (2) sonar image mosaics using stationary sonar scans with Smart Tether position data, (3) simultaneous localization and mapping (SLAM) while the vehicle was in motion, (4) SLAM using stationary sonar scans, (5) localization using previously created maps, and (6) SLAM while the vehicle was in motion with Smart Tether position data. Top-down-view maps of 22 different cisterns were successfully constructed. It is estimated that the cisterns were built as far back as 300 B.C., and few records of their size, shape, and connectivity existed before the expedition. 1.

Underwater exploration at Sistema Zacatón reached human limits when Jim Bowden descended to a depth of 289 meters April 6, 1994. In order to understand this karst system, unmanned robotic exploration is required to document the immense geometry of the underwater caves. The DEPTHX (DEep Phreatic THermal eXplorer) vehicle was developed with support from NASA to approach this problem, with the additional impetus to address robotic exploration in outer space. During the winter and spring of 2007, DEPTHX conducted three-dimensional (3D) underwater mapping missions of 4 cenotes in Sistema Zacatón: El Zacatón, Caracol, Poza Verde, and La Pilita. The detailed maps discovered no lateral tunnels connecting the cenotes, which are only 100-200 meters apart from each other. The deepest cenote, El Zacatón was bottomed out at a depth of 319 meters (339 m including above water cliff), making it the deepest underwater vertical shaft and second deepest underwater cave in the world. No side tunnels were discovered. However, the 3D data revealed geomorphic features of the cenotes to document how the karst system may have evolved through time. Spatial geochemical data collected contemporaneously during mapping missions indicate that water in the three warmest cenotes is extremely homogeneous, and the cooler cenote displays chemoclines similar to lacustrine type settings. The data collected by DEPTHX are being used with other types of geologic information to investigate the specific nature of hypogenic karstification that formed the cave system.

Abstract—In this paper we describe a novel algorithm for constructing a compact representation of 3D laser range data. Our approach extracts an alphabet of local scans from the scene. The words of this alphabet are used to replace recurrent local 3D structures, which leads to a substantial compression of the entire point cloud. We optimize our model in terms of complexity and accuracy by minimizing the Bayesian information criterion (BIC). Experimental evaluations on large realworld data show that our method allows robots to accurately reconstruct environments with as few as 70 words. I.

This paper documents the second of two archeological expeditions that employed several underwater robot mapping and localization techniques. The goal of this project is to explore and map ancient cisterns located on the islands of Malta and Gozo. Dating back to 300 B.C., the cisterns of interest acted as water storage systems for fortresses, private homes, and churches. They often consisted of several connected chambers, still containing water. A Remotely Operated Vehicle (ROV), was deployed into cisterns to obtain video and sonar images. Using a variety of sonar based mapping techniques, two-dimensional maps of 18 different cisterns were created. I.

This paper describes the application of using a submersible remotely operated vehicle (ROV) to map and explore underground water cisterns during a series of expeditions to Malta and Gozo. The purpose of this project was to create maps of ancient cisterns located under private homes, churches, and fortresses where passageways leading to the cisterns are too narrow and dangerous for humans to enter. These cisterns were used as water storage systems for hundreds of years, and many still contained water. The small ROV that was lowered into these cisterns was equipped with a sonar module to enable the creation of maps, two cameras to record live video, a grabber-arm for interacting with objects in the environment, and a Smart Tether to record additional positioning data of the ROV. Each of these components are discussed in terms of functionality and appropriateness for use by archaeologists wishing to explore and extract mapping information from narrow water-filled caverns. Additionally, three different mapping and localization techniques are presented including 1) Sonar image mosaics using stationary sonar scans, 2) Sonar image mosaics using stationary sonar scans with Smart Tether position data, and 3) Simultaneous Localization and Mapping (SLAM) using stationary sonar scans. Each of the algorithms used in this project have benefits in certain applications. During two expeditions in Malta and Gozo, 2-dimensional maps of 32 cisterns were successfully constructed. Categories and Subject Descriptors (according to ACM CCS): I.3.8 [Computer Graphics]: Applications—Cistern Visualization 1.

Abstract — Chemical properties of lake water can provide valuable insight into its ecology. Lakes that are permanently frozen over with ice are generally inaccessible to comprehensive exploration by humans. This paper describes the integration of several novel and existing technologies into an autonomous underwater robot, ENDURANCE, that was successfully used for gathering scientific data in West Lake Bonney in Taylor Valley, Antarctica, in December 2008. This paper focuses on three novel technological and algorithmic solutions. First, a robust position estimation system that uses an acoustic beacon to complement traditional deadreckoning is described. Second, a novel vision-based docking algorithm for locating and ascending a vertical shaft by tracking a blinking light source is presented. Third, a novel profiling system for measuring water properties while causing minimal water disturbance is described. Finally, experimental results from the scientific missions in 2008 in West Lake Bonney are presented. I.

Simultaneous localization and mapping (SLAM) has been shown to be feasible in many small, two-dimensional, structured domains. The next challenge is to develop real-time SLAM methods that enable robots to explore large, three-dimensional, unstructured environments and allow subsequent operation in these environments over long periods of time. To circumvent the scale limitations inherent in SLAM, the world can be divided up into more manageable pieces. SLAM can be formulated on these pieces by using a combination of metric submaps and a topological map of the relationships between submaps. The contribution of this paper is a realtime, three-dimensional SLAM approach that combines an evidence grid–based volumetric submap representation, a robust Rao–Blackwellized particle filter, and a topologically flexible submap segmentation framework and map representation. We present heuristic methods for deciding how to segment the world and for reconstructing large-scale metric maps for the purpose of closing loops. We demonstrate our method on a mobile robotic platform operating in large, three-dimensional environments. C ○ 2009 Wiley Periodicals, Inc. 1.