By Cyrill Stachniss
"Robotic Mapping and Exploration" is a vital contribution within the quarter of simultaneous localization and mapping (SLAM) for self reliant robots, which has been receiving loads of consciousness by way of the study neighborhood within the most up-to-date few years. The contents are thinking about the independent mapping studying challenge. ideas comprise uncertainty-driven exploration, energetic loop last, coordination of a number of robots, studying and incorporating historical past wisdom, and working with dynamic environments. effects are observed by way of a wealthy set of experiments, revealing a promising outlook towards the applying to quite a lot of cellular robots and box settings, reminiscent of seek and rescue, transportation projects, or automatic vacuum cleaning.
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Additional info for Robotic Mapping and Exploration
Example text
The grid cell that contains the robot location is initialized with 0, all others with ∞: Vx,y ←− 0, if (x, y) is the position of the robot ∞, otherwise 2. Update loop. For all grid cells cx,y do: Vx,y ←− min Vx+Δx,y+Δy + Δx2 + Δy 2 p(cx+Δx,y+Δy ) | Δx, Δy ∈ {−1, 0, 1} ∧ p(cx+Δx,y+Δy ) ∈ [0, occmax ] , where occmax is the maximum occupancy probability value of a grid cell the robot is allowed to traverse. This technique updates the value of all grid cells by the value of their best neighbors, plus the cost of moving to this neighbor.
The fact that a map generally contains several unexplored areas raises the problem of how to assign exploration tasks represented by frontier cells to the individual robots. If multiple robots are involved, we want to avoid that several of them move to the same location. To deal with these problems and to determine appropriate target locations for the individual robots, our system uses a decision-theoretic approach. We simultaneously consider the cost of reaching a frontier cell and the utility of that cell.
A coverage map cell is typically initialized using a uniform distribution in order to represent the maximum uncertainty about the actual state of the cell. 2 shows a typical coverage posterior we frequently obtain for partly occupied cells. 1. So far, we only explained the idea of coverage maps but left open how to actually determine the posterior based on observations. In the next section, we describe how we can update coverage maps based on sensory input. 3 Updating Coverage Maps Upon Sensory Input To update a coverage map whenever sensor data arrives, we apply a Bayesian update scheme.