Geolocalization & Mapping

Simultaneous localization and mapping

Herbanatur has specialized for nearly two decades in the in computational geometry, simultaneous localization and mapping is the computational problem of constructing or updating a map of an unknown environment while simultaneously keeping track of an agent’s location within it.

Building a map, localizing within the map, and planning using the map are fundamental problems for mobile robotics. Every mobile robotic system must incorporate some type of solution to all three problems.
While the interdependency between mapping and localization is well known as the Simultaneous Localization and Mapping (SLAM) problem, there is a growing understanding in the research community that planning how the robot goes about mapping and exploring an environment (and operating in the environment afterwards) can avoid degenerate conditions and significantly reduce complexity of SLAM. Thus the task of exploring a new environment combines all
three problems, since the robot must plan to find actions that reduce uncertainty in both mapping and localization. This combined problem is known as Active SLAM.

Independently, SLAM and planning have been solved in small, two dimensional, structured domains. Robots need to move beyond these simple environments. The challenge is to develop real-time Active SLAM methods that allow robots to explore large, three dimensional, unstructured environments, and allow subsequent operation in these environments over long periods oftime.

But scaling up to truly large environments requires a second key insight beyond Active SLAM: to circumvent the scale limitations inherent in SLAM, the world can be divided up into more manageable pieces, or submaps. The SLAM problem then becomes a segmented SLAM
problem, which represents the world with a combined metric and topological map, building metric submaps as necessary and refining the topological relationships between submaps.

The contribution of this thesis is a real-time Active SLAM approach that combines a novel evidence grid-based volumetric representation, a robust Rao-Blackwellized particle-filter, a topologically flexible submap segmentation framework, and an integrated stochastic planning method
for reducing SLAM uncertainty and predicting possible loop closures based on local map structure. We demonstrate our methods on several robotic platforms in both structured and unstructured large, three dimensional environments.

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