A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
International Journal of Computer Vision
FastSLAM: a factored solution to the simultaneous localization and mapping problem
Eighteenth national conference on Artificial intelligence
DP-SLAM: fast, robust simultaneous localization and mapping without predetermined landmarks
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
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Being able to assess the performance of the algorithmic components of unmanned autonomous systems is a necessity. Defining repeatable and commonly shared test protocols to assess the performance of the algorithms involved in autonomy is the key to achieve standardization in the unmanned autonomous systems field (Unmanned Ground Vehicles (UGVs) and Unmanned Aerial Vehicles (UAVs)). This paper proposes a generic methodology to evaluate any function of an autonomous system and illustrates the methodology on two examples: for the evaluation of visual beacon tracking algorithm and for the evaluation of Simultaneous Localization And Mapping (SLAM) algorithms. The lessons learnt from these evaluations are then described.