Abstract

The ability to localise a camera moving in a previously unknown en- vironment is desirable for a wide range of applications. In computer vision this problem is studied as monocular SLAM. Recent years have seen improvements to the usability and scalability of monocular SLAM systems to the point that they may soon find uses outside of laboratory conditions. However, the robustness of these systems to rapid camera motions (we refer to this quality as agility) still lags behind that of tracking systems which use known object models. In this paper we attempt to remedy this. We present two approaches to improving the agility of a keyframe-based SLAM system: Firstly, we add edge features to the map and exploit their resilience to motion blur to improve tracking under fast motion. Sec- ondly, we implement a very simple inter-frame rotation estimator to aid tracking when the camera is rapidly panning – and demonstrate that this method also en- ables a trivially simple yet effective relocalisation method. Results show that a SLAM system combining points, edge features and motion initialisation allows highly agile tracking at a moderate increase in processing time.