We present an improved version of the treemap SLAM algorithm which uses Cholesky factors for representing Gaussians and a Hierarchical Tree Partitioning algorithm derived from the established Kernighan-Lin heuristic for graph bisection. We demonstrate the algorithm's efficiency by mapping a simulated building with 1032271 landmarks. In the end, we close a million-landmarks loop in 21ms, providing an estimate for approx=10000 selected landmarks close to the robot, or in 442ms for computing a full estimate.
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