The underwater domain provides a wide range of potential applications for autonomous systems. Sessile (im-mobile) sensor platforms can provide a sensing network to monitor a range of different underwater events. Monitoring such networks can be a challenge, however, as the sensor nodes can be difficult to monitor and the nature of the medium limits wireless communication. Here we describe an approach that uses an autonomous underwater vehicle to monitor the state of sessile sensors. A visual communication channel is established from the sensor node to the robot that can then communicate the state of the sensor to an underwater-or surface-based operator. This paper describes the basic approach and results of preliminary experiments.

For systems to become truly autonomous it is necessary that they be able to interact with complex real-world environments. In this article we investigate techniques and technologies to address the problem of the acquisition and representation of complex environments such as those found underwater. The underwater environment presents many challenges for robotic sensing including highly variable lighting and the presence of dynamic objects such as fish and suspended particulate matter. The dynamic six-degree-of-freedom nature of the environment presents further challenges due to unpredictable external forces such as current and surge. In order to address the complexities of the underwater environment we have developed a stereo vision-inertial sensing device that has been successfully deployed to reconstruct complex 3-D structures in both the aquatic and terrestrial domains. The sensor combines 3-D information, obtained using stereo vision, with 3DOF inertial data to construct 3-D models of the environment. Semiautomatic tools have been developed to aid in the conversion of these representations into semantically relevant primitives suitable for later processing. Reconstruction and segmentation of underwater structures obtained with the sensor are presented.

Recent advancements in laser and visible light sensor technology allows for the collection of photorealistic 3D scans of large scale spaces. This enables the technology to be used in real world applications such as crime scene investigation. The 3D models of the environment obtained with a 3D scanner capture visible surfaces but do not provide semantic information about salient features within the captured scene. Later processing must convert these raw scans into salient scene structure. This paper describes ongoing research into the generation of semantic data from the 3D scan of a crime scene to aid forensic specialists in crime scene investigation and analysis.

The development of sensors capable of obtaining 3D scans of crime scenes is revolutionizing the ways in which crime scenes can be analyzed and at the same time is driving the need for the development of sophisticated tools to represent and store this data. Here we describe the design of a multimedia database suitable for representing and reasoning about crime scene data. The representation is grounded in the physical environment that makes up the crime scene and provides mechanisms for representing both traditional (forms-based) data as well as 3D scan and other complex spatial data.

The underwater environment presents many challenges for robotic sensing including highly variable lighting and the presence of dynamic objects such as fish and suspended particulate matter. The dynamic six-degree-of-freedom nature of the environment presents further challenges due to unpredictable external forces such as current and surge. Despite these challenges the aquatic environment presents many real and practical applications for robotic systems. A common requirement of many of these tasks is the need to construct accurate 3D representations of specific environmental structures. In order to address these needs we have developed a stereo vision inertial sensing device that has been successfully deployed to reconstruct complex 3D structures in both the aquatic and terrestrial domains. The sensor combines 3D information, obtained using stereo vision algorithms, with 3DOF inertial data to construct 3D models of the environment. The resulting model representation is then converted to a textured polygonal mesh for later processing. Semi-automatic tools have been developed to aid in the processing of these representations. Reconstruction and segmentation of coral and other underwater structures obtained with the sensor are presented.

The development of sensors capable of obtaining 3D scans of crime scenes is revolutionizing the ways in which crime scenes can be analyzed and at the same time is driving the need for the development of sophisticated tools to repre- sent and store this data. Here we describe the design of a multimedia database suitable for representing and reason- ing about crime scene data. The representation is grounded in the physical environment that makes up the crime scene and provides mechanisms for representing both traditional (forms-based) data as well as audiovisual (3D scan and other complex spatial data). Some initial results obtained with tools designed to seed the dataset are reported as well as some components of the final database system.