ICRA 2011 Paper Abstract

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Paper TuP1-InteracInterac.15

Lategahn, Henning (Karlsruhe Institute of Technology), Geiger, Andreas (Karlsruhe Institute of Technology), Kitt, Bernd (Karlsruhe Institute of Technology)

Visual SLAM for Autonomous Ground Vehicles

Scheduled for presentation during the Poster Sessions "Interactive Session II: Systems, Control and Automation" (TuP1-InteracInterac), Tuesday, May 10, 2011, 13:40−14:55, Hall

2011 IEEE International Conference on Robotics and Automation, May 9-13, 2011, Shanghai International Conference Center, Shanghai, China

This information is tentative and subject to change. Compiled on April 2, 2020

Keywords Computer Vision for Robotics and Automation, SLAM, Mapping

Abstract

Simultaneous Localization and Mapping (SLAM) and Visual SLAM (V-SLAM) in particular have been an active area of research lately. In V-SLAM the main focus is most often laid on the localization part of the problem allowing for a drift free motion estimate. To this end, a sparse set of landmarks is tracked and their position is estimated. However, this set of landmarks (rendering the map) is often too sparse for tasks in autonomous driving such as navigation, path planning, obstacle avoidance etc. Some methods keep the raw measurements for past robot poses to address the sparsity problem often resulting in a pose only SLAM akin to laser scanner SLAM. For the stereo case, this is however impractical due to the high noise of stereo reconstructed point clouds. In this paper we propose a dense stereo V-SLAM algorithm that estimates a dense 3D map representation which is more accurate than raw stereo measurements. Thereto, we run a sparse V-SLAM system, take the resulting pose estimates to compute a locally dense representation from dense stereo correspondences. This dense representation is expressed in local coordinate systems which are tracked as part of the SLAM estimate. This allows the dense part to be continuously updated. Our system is driven by visual odometry priors to achieve high robustness when tracking landmarks. Moreover, the sparse part of the SLAM system uses recently published sub mapping techniques to achieve constant runtime complexity most of the time. The improv

 

 

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