Dense depth maps play an important role in Computer Vision and AR (Augmented Reality). For CV applications, a dense depth map is the cornerstone of 3D reconstruction allowing real objects to be precisely displayed in the computer. And Dense depth maps can handle correct occlusion relationships between virtual content and real objects for better user experience in AR. However, the complicated computation limits the development of computing dense depth maps. We present a novel algorithm that produces low latency, spatio-temporally smooth dense depth maps using only a CPU. The depth maps exhibit sharp discontinuities at depth edges in low computational complexity ways. Our algorithm obtains the sparse SLAM reconstruction first, then extracts coarse depth edges from a down-sampled RGB image by morphology operations. Next, we thin the depth edges and align them with image edges. Finally, an effective initialization scheme and an improved optimization solver are adopted to accelerate convergence. We evaluate our proposal quantitatively and the result shows improvements on the accuracy of depth map with respect to other state-of-the-art and baseline techniques.

Autonomous vehicles require precise localization for safe control. This paper presents a localization approach based on semantic map and visual inertial odometry for autonomous vehicles. Our approach uses consumer grade parts, and only relies on a single front camera and a consumer grade IMU and a GPS. Using real-time semantic landmark detection and real-time visual inertial odometry, we localize the full 6-DOF pose of the vehicle in the semantic map with mean absolute accuracy at less than 20 cm, With this accuracy, we can achieve high levels of autonomy, and speed up the evolution of autonomous driving. The main contributions of our approach are: (i) 2D-3D semantic landmark matching in continuous frames; (ii) full 6-DOF pose optimization with semantic constraints in a sliding time window.