语义SLAM的思考
近些年，SLAM技术已经获得了突飞猛进的发展，SLAM技术在工业机器人，AR，VR技术，以及智能车等方面都有着广大的应用前途。SLAM技术完成了智能体（对SLAM主体的统称）对环境的几何信息的理解，但是忽略了对环境语义信息的理解。单纯的SLAM技术是缺乏场景理解能力的，智能体实时的对3D环境感知理解能力是智能体的技术的关键部分。
ORB-SLAM，LSD-SLAM，DSO等方法已经能够帮助智能体对大场景环境获得实时的几何信息，视觉SLAM可以实时的构建世界3D地图，并且实时估计智能体的位置以及朝向。SLAM算法与Deeping learning，cnn是互补的，SLAM关注于世界的几何信息，后者关注智能体对于世界的认知。如果你想让机器人去桌子附近，不要碰撞，可以使用SLAM，但是如果你想让机器人去桌子上拿苹果，就离不开CNN。
并且SLAM与语义也是互补的关系，语义帮助SLAM减缓对特征的依赖，对地图进行更高层次的理解，语义信息一定可以提高SLAM的鲁班程度。而SLAM也可以帮助语义，SLAM获取的几何信息也是方便机器人进行语义理解的重要内容。随着时间的发展，两者必然会擦出火花。
While self-driving cars are one of the most important applications of SLAM, according to Andrew Davison, one of the workshop organizers, SLAM for Autonomous Vehicles deserves its own research track. (And as we'll see, none of the workshop presenters talked about self-driving cars). For many years to come it will make sense to continue studying SLAM from a research perspective, independent of any single Holy-Grail application. While there are just too many system-level details and tricks involved with autonomous vehicles, research-grade SLAM systems require very little more than a webcam, knowledge of algorithms, and elbow grease. As a research topic, Visual SLAM is much friendlier to thousands of early-stage PhD students who’ll first need years of in-lab experience with SLAM before even starting to think about expensive robotic platforms such as self-driving cars.
对于智能车而言，我也相信DL必将在每一个智能车里面帮助智能车对环境进行理解，哪里是可行驶区域，前方的“障碍物”是车还是人，交通标志的含义是什么，这些都需要DL来解决，
