Simultaneous localization and mapping (SLAM) via a monocular camera is a key enabling technique for many augmented reality (AR) applications. In this work, we present a monocular SLAM system which can provide real-time dense mapping even for challenging poorly-textured regions based on the piecewise planarity approximation. Specifically, our system consists of three modules. First, a tracking module based on the direct method [3] continuously estimates camera poses with respect to the scene. Second, a semi-dense mapping module takes the estimated camera pose as input and calculates depths of highly-textured pixels based on pixel matching and triangulation. Third, dense mapping module approximates textureless regions identified by a homogeneous-color region detector using piecewise plane models. The 3D piecewise planes are reconstructed via the proposed multi-plane segmentation and multi-plane fusion algorithms. Live experiments in a real AR demo with a hand-held camera demonstrate the effectiveness and efficiency of our method in practical scenario.

Monocular simultaneous localization and mapping (SLAM) is a key enabling technique for many augmented reality (AR) applications. However, conventional methods for monocular SLAM can obtain only sparse or semi-dense maps in highly-textured image areas. Poorly-textured regions which widely exist in indoor and man-made urban environments can be hardly reconstructed, impeding interactions between virtual objects and real scenes in AR apps. In this paper,we present a novel method for real-time monocular dense mapping based on the piecewise planarity assumption for poorly textured regions. Specifically, a semi-dense map for highly-textured regions is first calculated by pixel matching and triangulation [6, 7]. Large textureless regions extracted by Maximally Stable Color Regions (MSCR) [11], which is a homogeneous-color region detector, are approximated using piecewise planar models which are estimated by the corresponding semi-dense 3D points and the proposed multi-plane segmentation algorithm. Plane models associated with the same 3D area across multiple overlapping views are linked and fused to ensure a consistent and accurate 3D reconstruction. Experimental results on two public datasets [15, 23] demonstrate that our method is 2.3X~2.9X faster than the state-of-the-art method DPPTAM [2], and meanwhile achieves better reconstruction accuracy and completeness. We also apply our method to a real AR application and live experiments with a hand-held camera demonstrate the effectiveness and efficiency of our method in practical scenario.

This paper aims at accurate and efficient pose tracking of planar targets on modern smartphones. Existing methods, relying on either visual features or motion sensing based on built-in inertial sensors, are either too computationally expensive to achieve realtime performance on a smartphone, or too noisy to achieve sufficient tracking accuracy. In this paper we present a hybrid tracking method which can achieve real-time performance with high accuracy. Based on the same framework of a state-of-the-art visual feature tracking algorithm [5] which ensures accurate and reliable pose tracking, the proposed hybrid method significantly reduces its computational cost with the assistance of a phone's built-in inertial sensors. However, noises in inertial sensors and abrupt errors in feature tracking due to severe motion blurs could result in instability of the hybrid tracking system. To address this problem, we propose to employ an adaptive Kalman filter with abrupt error detection to robustly fuse the inertial and feature tracking results. We evaluated the proposed method on a dataset consisting of 16 video clips with synchronized inertial sensing data. Experimental results demonstrated our method's superior performance and accuracy on smartphones compared to a state-of-the-art vision tracking method [5]. The dataset will be made publicly available with the publication of this paper.

This paper aims at robust and efficient pose tracking for augmented reality on modern smartphones. Existing methods, relying on either vision analysis or motion sensing, are either too computationally expensive to achieve real-time performance on a smartphone, or too noisy to achieve sufficient robustness. This paper presents a hybrid tracking system which can achieve real-time performance with high robustness. Our system utilizes an efficient featureless method based on pixel-based registration to track the object pose on every frame. The featureless tracking result is revised from time to time by a feature-based method to reduce tracking errors. Both featureless and feature-based tracking results are sensitive to large motion blurs. To improve the robustness, an adaptive Kamlan filter is proposed to fuse the visual tracking results with the inertial tracking results computed form phone's built-in sensors. Our hybrid method is evaluated on a dataset consisting of 16 video clips with synchronized inertial sensing data. Experimental results demonstrated the superior performance of our method to state-of-the-art visual tracking methods [5, 12] on smartphones. The dataset will be made publicly available with the publication of this paper.