Efficiently Increasing Map Density in Visual SLAM Using Planar Features with Adaptive Measurement

The visual simultaneous localisation and mapping (SLAM) systems now in widespread use are based on localised point features [2, 4, 5]. Although effective in many respects, the approach has limitations when considering the density and efficiency of map representation. With a dense population of features, camera tracking can be robust, able to withstand significant occlusion and large changes in camera viewpoint. But this comes at a high computational cost, typically increasing quadratically with the number of features. In this work we propose increasing map density by building in higherorder structure in the form of planar features. An important and novel aspect of the work is the manner in which the planar features are updated and used to localise the camera. We base our approach on an extended Kalman filter (EKF) monocular SLAM system developed by Chekhlov et al. [3]. This provides real-time estimates of the 3-D pose of a calibrated camera whilst simultaneously mapping the scene in terms of point based features. In order to incorporate planar structure into the real-time monocular SLAM we carry out three steps: detection of planar structure in the scene; insertion of planar features into the map; and adaptive measurement of the features. To apply the principle of adaptive measurement it is essential that planar features inserted into the map correspond to actual planar structure in the scene. For this we employ the method proposed by Martínez-Carranza and Calway [6], which uses an appearance model to detect planes defined by subsets of mapped point features (at least three points). Having detected planar features in the scene these are inserted into the map using a suitable representation within the filter state. This has two components: plane parameterisation and the reference camera. The plane is defined by yp = (θ ,φ ,ρ), where (θ ,φ) defines the unit normal of the plane in polar coordinates in the reference camera and ρ is the inverse depth of the plane centre along the ray defined by uo, with the latter being stored at initialisation of the plane, see figure 1a. Insertion of the reference camera is done by augmenting the state with a copy of the current pose, i.e. vp = v, and with initialised plane parameters yp derived from the pose and the subset of mapped points which define the plane. The reference camera serves two purposes: it references the plane in the SLAM coordinate system (with the associated uncertainties) and enables subsequent measurement of the planar feature using region based matching with respect to the current frame (key frame). To facilitate the latter the key frame image is also stored in the system. As illustrated in figure 1b, measurements for a planar feature are therefore assumed to take the following form: