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The reconstruction of a 3D map is the key point of any SLAM algorithm. Traditionally these maps are built using non-linear minimization techniques, which need a lot of computational resources. In this paper we present a highly paralellizable stochastic approach that fits very well on the graphics hardware. It can achieve the same precision as non-linear optimization methods without loosing the real time performance. Results are compared against the well known Levenberg-Marquardt algorithm using real video sequences.

GPU OPTIMIZER: A 3D RECONSTRUCTION ON THE GPU USING MONTE CARLO SIMULATIONS - How to Get Real Time without Sacrificing Precision