Vision-based ego-lane inference using High-Definition (HD) maps is essential in autonomous driving and advanced driver assistance systems. The traditional approach necessitates well-calibrated cameras, which confines variation of camera configuration, as the algorithm relies on intrinsic and extrinsic calibration. In this paper, we propose a learning-based ego-lane inference by directly estimating the ego-lane index from a single image. To enhance robust performance, our model incorporates the two-head structure inferring ego-lane in two perspectives simultaneously. Furthermore, we utilize an attention mechanism guided by vanishing point-and-line to adapt to changes in viewpoint without requiring accurate calibration. The high adaptability of our model was validated in diverse environments, devices, and camera mounting points and orientations.

Concurrent mapping necessitates data association among vehicles to overcome temporal and sensor modality differences. In this work, we focus on an underwater multi-vehicle mapping scenario in which vehicles have various sensor modalities, namely sonar and camera. This inter-session sonar-optical image matching poses two main challenges. First, ensuring covisibility for the opti-acoustic pair is complex due to their projection models and field of view (FOV) difference. Second, even with secured covisible frames, feature matching over various sensor modalities is not trivial. To overcome these challenges, we complete multi-session simultaneous localization and mapping (SLAM) by introducing an opti-acoustic pairwise factor. We alleviate the covisibility requirement by introducing inter-session measurements. We achieved opti-acoustic feature matching by applying a style-transfer and integration with SuperGlue. The proposed method is validated via simulation and real underwater tank tests.

Estimating a dense and accurate depth map is the key requirement for autonomous driving and robotics. Recent advances in deep learning have allowed depth estimation in full resolution from a single image. Despite this impressive result, many deep-learning-based monocular depth estimation (MDE) algorithms have failed to keep their accuracy yielding a meter-level estimation error. In many robotics applications, accurate but sparse measurements are readily available from Light Detection and Ranging (LiDAR). Although they are highly accurate, the sparsity limits full resolution depth map reconstruction. Targeting the problem of dense and accurate depth map recovery, this paper introduces the fusion of these two modalities as a depth completion (DC) problem by dividing the role of depth inference and depth regression. Utilizing the state-of-the-art MDE and our Gaussian process (GP) based depth-regression method, we propose a general solution that can flexibly work with various MDE modules by enhancing its depth with sparse range measurements. To overcome the major limitation of GP, we adopt Kernel Interpolation for Scalable Structured (KISS)-GP and mitigate the computational complexity from O(N3) to O(N). Our experiments demonstrate that the accuracy and robustness of our method outperform state-of-the-art unsupervised methods for sparse and biased measurements.

SHADOWis a middleware for building GUI applications in home digital media devices. It is a part of home software platform that solves portability issues and it provides a universal application framework for OS independence. It also supports C++ class libraries for object-oriented design of GUI applications. SHADOW was successfully used for building GUI applications for TV-like home devices. In this paper, we describe how SHADOW is designed and implemented in order to support home applications.