A tesselated probabilistic representation for spatial robot perception and navigation

The ability to recover robust spatial descriptions from sensory information and to efficiently utilize these descriptions in appropriate planning and problem-solving activities are crucial requirements for the development of more powerful robotic systems. Traditional approaches to sensor interpretation, with their emphasis on geometric models, are of limited use for autonomous mobile robots operating in and exploring unknown and unstructured environments. Here, researchers present a new approach to robot perception that addresses such scenarios using a probabilistic tesselated representation of spatial information called the Occupancy Grid. The Occupancy Grid is a multi-dimensional random field that maintains stochastic estimates of the occupancy state of each cell in the grid. The cell estimates are obtained by interpreting incoming range readings using probabilistic models that capture the uncertainty in the spatial information provided by the sensor. A Bayesian estimation procedure allows the incremental updating of the map using readings taken from several sensors over multiple points of view. An overview of the Occupancy Grid framework is given, and its application to a number of problems in mobile robot mapping and navigation are illustrated. It is argued that a number of robotic problem-solving activities can be performed directly on the Occupancy Grid representation. Some parallels are drawn between operations on Occupancy Grids and related image processing operations

Mapping and Characterizing Subtidal Oyster Reefs Using Acoustic Techniques, Underwater Videography and Quadrat Counts

Populations of the eastern oyster Crassostrea virginica have been in long-term decline in most areas. A major hindrance to effective oyster management has been lack of a methodology for accurately and economically obtaining data on their distribution and abundance patterns. Here, we describe early results from studies aimed at development of a mapping and monitoring protocol involving acoustic techniques, underwater videography, and destructive sampling (excavated quadrats). Two subtidal reefs in Great Bay, New Hampshire, were mapped with side-scan sonar and with videography by systematically imaging multiple sampling cells in a grid covering the same areas. A single deployment was made in each cell, and a 5-10-s recording was made of a 0.25-m2 area; the location of each image was determined using a differential global position system. A still image was produced for each of the cells and all (n = 40 or 44) were combined into a single photomontage overlaid onto a geo-referenced base map for each reef using Arc View geographic information system. Quadrat (0.25 m2 ) samples were excavated from 9 or 10 of the imaged areas on each reef, and all live oysters were counted and measured. Intercomparisons of the acoustic, video, and quadrat data suggest: (1) acoustic techniques and systematic videography can readily delimit the boundaries of oyster reefs; (2) systematic videography can yield quantitative data on shell densities and information on reef structure; and (3) some combination of acoustics, systematic videography, and destructive sampling can provide spatially detailed information on oyster reef characteristics