Combining dense short range sensors and sparse long range sensors for mapping
Abstract: Mapping is one of the main components of autonomous robots, and consist in the construction of a model of their environment based on the information gathered by different sensors over time. Those maps will have different attributes depending on the type of sensor used for the reconstruction. In this thesis we focus on RGBD cameras and LiDARs. The acquired data with cameras is dense, but the range is short and the construction of large scale and consistent maps is more challenging. LiDARs are the exact opposite, they give sparse data but can measure long ranges accurately and therefore support large scale mapping better. The thesis presents a method that uses both types of sensors with the purpose of combine their strengths and reduce their weaknesses. The evaluation of the system is done in an indoor environment, and with an autonomous robot. The result of the thesis shows a map that is robust in large environments and has dense information of the surroundings.
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