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One of the major challenges in multi-robot exploration is to fuse the partial maps generated by individual robots into a consistent global map. We address 3D volumetric map fusion by extending the well known iterative closest point(ICP) algorithm to include probabilistic distance and surface information. In addition, the relative transformation is evaluated based on Mahalanobis distance and map dissimilarities...
Majority of the autonomous robot exploration strategies operate by iteratively extracting the boundary between the mapped open space and unexplored space, frontiers, and sending the robot towards the “best” frontier. Traditional approaches process the entire map to retrieve the frontier information at each decision step. This operation however is not scalable to large map sizes and high decision frequencies...
Traditional frontier based exploration strategies operate by iteratively selecting the next best sensing location myopically and moving to the specified location, until the entire environment is explored. And it does not consider the new information added to the map through continuous observations by the robot along the way to a selected location. This can sometimes lead to redundant traversal by...
Autonomously navigating a robot in an unknown environment filled with obstacles to a goal location using a directed exploration strategy is presented. The exploration is directed towards the goal by iteratively selecting intermediate target points in the environment that reduces the distance estimate to the goal from robot's current position, until the goal is reached. A repetitive rechecking approach...
The most common way to select target points for sensing during autonomous robot exploration is to employ the concept of frontiers. Frontiers in an occupancy grid map are defined as the boundaries between grid cells categorized as free and unknown. Most of the current single and multi-robot exploration algorithms have focused on developing efficient exploration and coordination strategies, and have...
Trade-off between the information gain and cost has been extensively used as an evaluation criteria for target points in robot exploration strategies where the primary goal is to reduce the mission time. This article introduces a new gain function that has an integrated cost component that can be used in exploration strategies to map the terrain in a balanced way in all directions to generate compact...
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