Joint depth estimation and multi-model SLAM for robust perception in structure-degraded environments

Interactions between humans or humanoids and their environment through tasks like grasping or manipulation typically require accurate depth information. The human vision system integrates various monocular and binocular depth estimation mechanisms in order to achieve robust and reliable depth perception. Such an integrated approach can be applied to humanoid depth perception. Integration requires a knowledge of the characteristics of the methods being combined. Three different methods incorporating active vision (stereo disparity, vergence and familiar size) were statistically examined and combinations of these methods based on this statistical examination were investigated. We found evidence that active vision provides better depth estimations than the standard static-parallel stereo methods examined within interaction range and therefore is better suited for tasks like reaching, grasping and manipulation. We also demonstrate that a combination of methods have the potential to increase the accuracy of estimations.