Visual Perception of 3D Worlds for Haptic Exploration

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We live in a very visual world; we rely chiefly on our eyes to tell us where we are and what is around us and then make decisions of where to go and how to get there based on that sensed information. Even traffic signs and indicators rely on us using our eyes. GPS (Global Positioning Sensors) have revolutionized personal navigation by providing us with another sensor for telling us where we are. Unfortunately a GPS requires a prior map as well as clear sight of at least 3 GPS satellites in order to triangulate our position. What if we had no map of our environment to begin with or we could not rely on a GPS to tell us where we were? A camera is a sensor that closely resembles our eyes. Visual SLAM (Simultaneous Localization And Mapping) is the problem of building a map and localizing at the same time without a prior map by only using a camera. Visual SLAM is a relatively new research area that has shown great promise but is hindered by the problem of robustness and being computational tractable. Current methods rely on single feature points which accumulate exponentially, especially in real world environments. We are proposing to use real world objects (or clusters of features) as the landmarks as opposed to feature points. We have already demonstrated that triads of feature points can lead to better object detection & recognition. Using these triads of points or the objects themselves can lead to meaningful compact SLAM maps and address current shortcomings. We also propose other improvements to visual SLAM that come from techniques used in Structure From Motion (SFM) techniques such as filtering or error modelling.