Visual Perception of 3D Worlds for Haptic Exploration

我们生活在一个非常直观的世界里；我们主要依靠眼睛来告诉我们我们在哪里，我们周围有什么，然后根据感知到的信息做出去哪里和怎么去的决定。即使是交通标志和指示器也依赖于我们的眼睛。GPS(全球定位传感器)为我们提供了另一个告诉我们所在位置的传感器，从而使个人导航发生了革命性的变化。不幸的是，GPS需要事先有地图，以及至少3颗GPS卫星的清晰视野，才能对我们的位置进行三角测量。如果我们一开始就没有环境地图，或者我们不能依靠GPS来告诉我们我们在哪里，那该怎么办？相机是一种传感器，与我们的眼睛非常相似。视觉SLAM(同时定位和测绘)是指在没有事先地图的情况下，仅使用相机来构建地图并同时进行定位的问题。视觉SLAM是一个相对较新的研究领域，已经显示出很大的前景，但由于健壮性和计算简便性的问题而受到阻碍。当前的方法依赖于呈指数级累积的单个特征点，特别是在真实环境中。我们建议使用真实世界的对象(或特征簇)作为地标，而不是特征点。我们已经证明，三个特征点的组合可以带来更好的目标检测和识别。使用这三个点或对象本身可以产生有意义的紧凑SLAM地图，并解决当前的缺点。我们还提出了对视觉SLAM的其他改进，这些改进来自于运动结构(SFM)技术中使用的技术，如过滤或错误建模。

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.