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2D-SDF-SLAM: A signed distance function based SLAM frontend for laser scanners | IEEE Conference Publication | IEEE Xplore
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2D-SDF-SLAM: A signed distance function based SLAM frontend for laser scanners


Abstract:

We introduce a novel approach to simultaneous localization and mapping for robots equipped with a 2D laser scanner. In particular, we propose a fast scan registration alg...Show More

Abstract:

We introduce a novel approach to simultaneous localization and mapping for robots equipped with a 2D laser scanner. In particular, we propose a fast scan registration algorithm that operates on 2D maps represented as a signed distance function (SDF). Using SDFs as a map representation has several advantages over existing approaches: while classical 2D scan matchers employ brute-force matching to track the position of the robot, signed distance functions are differentiable on large parts of the map. Consequently, efficient minimization techniques such as Gauss-Newton can be applied to find the minimum. In contrast to occupancy grid maps, the environment can be captured with sub-grid cell size precision, which leads to a higher localization accuracy. Furthermore, SDF maps can be triangulated to polygon maps for efficient storage and transfer. In a series of experiments, conducted both in simulation and on a real physical platform, we demonstrate that SDF tracking is more accurate and efficient than previous approaches. We outperform scan matching on occupancy maps in simulation by ~270% in terms of root mean squared deviation (RMSD) with a ~63% lower standard deviation. In the real robot experiments, we obtain a performance advantage of ~14% RMSD with a ~25% lower standard deviation.
Date of Conference: 28 September 2015 - 02 October 2015
Date Added to IEEE Xplore: 17 December 2015
ISBN Information:
Conference Location: Hamburg, Germany

References

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