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I am working on SLAM for autonomous car like vehicles with 2D lasers and IMU (deriving odometry).

I would like to know how efficient is using the existing SLAM algorithms (for example: gmapping in ROS based on rao blackwellized particle filter).

till now i find MAP are high in volume and speed of vehicle is high and most importantly computational time compared to Mobile robots.

Are there any other important factors to consider for car like vehicles in using SLAM algorithm.

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I think you should look comparison of SLAM algorithms. There is a benchmark list for visual odoemetry/SLAM in Karlsruhe Institue of Technology named KITTI Vision Benchmark Suite. Their bench marking includes Stereo vision and Laser Points

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Gmapping is a good algorithm if you can modify it to meet your needs.

"I would like to know how efficient is using the existing SLAM algorithms" ?

If you're new to the world of slam, you'd better use already developed algorithms or use one of them as a starting base.

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