Augmented Reality (AR) brings immersive experiences to users. With recent advances in computer vision and mobile computing, AR has scaled across platforms, and has increased adoption in major products. One of the key challenges in enabling AR features is proper anchoring of the virtual content to the real world, a process referred to as tracking. In this paper, we present a system for motion tracking, which is capable of robustly tracking planar targets and performing relative-scale 6DoF tracking without calibration. Our system runs in real-time on mobile phones and has been deployed in multiple major products on hundreds of millions of devices.
Accurate initialization improves the resilience of SLAM algorithms and makes optimization converge faster. Researchers have relied on Structure-from-Motion (SfM) techniques, rotation averaging @cite_15 @cite_3 , or closed-form solutions @cite_9 to initialize the camera trajectories and the world map. However, these techniques still require parallax-inducing motion and accurate calibration, rendering them problematic for instant AR placement.
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The initialization is one of the less reliable pieces of Visual-Inertial SLAM (VI-SLAM) and Odometry (VI-O). The estimation of the initial state (camera poses, IMU states and landmark positions) from the first data readings lacks the accuracy and robustness of other parts of the pipeline, and most algorithms have high failure rates and or initialization delays up to tens of seconds. Such initialization is critical for AR systems, as the failures and delays of the current approaches can ruin the user experience or mandate impractical guided calibration. In this paper we address the state initialization problem using a monocular-inertial sensor setup, the most common in AR platforms. Our contributions are 1) a general linear formulation to obtain an initialization seed, and 2) a non-linear optimization scheme, including gravity, to refine the seed. Our experimental results, in a public dataset, show that our approach improves the accuracy and robustness of current VI state initialization schemes. Pose graph optimization is the non-convex optimization problem underlying pose-based Simultaneous Localization and Mapping (SLAM). If robot orientations were known, pose graph optimization would be a linear least-squares problem, whose solution can be computed efficiently and reliably. Since rotations are the actual reason why SLAM is a difficult problem, in this work we survey techniques for 3D rotation estimation. Rotation estimation has a rich history in three scientific communities: robotics, computer vision, and control theory. We review relevant contributions across these communities, assess their practical use in the SLAM domain, and benchmark their performance on representative SLAM problems (Fig. 1). We show that the use of rotation estimation to bootstrap iterative pose graph solvers entails significant boost in convergence speed and robustness.