Abstract: Conventional self-supervised monocular depth prediction methods are based on a static environment assumption, which leads to accuracy degradation in dynamic scenes due to the mismatch and occlusion problems introduced by object motions.
Abstract: Self-supervised monocular depth prediction provides a cost-effective solution to obtain the 3D location of each pixel. However, the existing approaches usually lead to unsatisfactory accuracy, which is critical for autonomous robots.
Abstract: In recent years, semi-supervised learning has been widely explored and shows excellent data efficiency for 2D data. There is an emerging need to improve data efficiency for 3D tasks due to the scarcity of labeled 3D data.
Abstract: Embodiments described herein may provide a method for generating a three-dimensional vector model of the interior of a structure. Methods may include: receiving sensor data indicative of a trajectory; receiving sensor data defining structural surfaces within a structure; generating a three-dimensional point cloud from the sensor data defining structural surfaces within the structure; segmenting the three-dimensional point cloud into two or more segments based, at least in part, on the sensor data indicative of trajectory; generating a three-dimensional surface model of an interior of the structure based on the segmented three-dimensional point cloud with semantic recognition and labelling; and providing the three-dimensional surface model of an interior of the structure to an advanced driver assistance system to facilitate autonomous vehicle parking.
Abstract: This paper presents a novel metric inspection robot system using a deep neural network to detect and measure surface flaws (i.e., crack and spalling) on concrete structures performed by a wall-climbing robot.
Abstract: This paper presents a new holistic vision-based mobile assistive navigation system to help blind and visually impaired people with indoor independent travel. The system detects dynamic obstacles and adjusts path planning in real-time to improve navigation safety.
Abstract: In this paper, we exploit the concrete surface flaw inspection through the fusion of visual positioning and semantic segmentation approach. The fused inspection result is represented by a 3D metric map with a spatial area, width, and depth information, which shows the advantage over general inspection in image space without metric info.