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: Accurate localization on the autonomous driving cars is essential for autonomy and driving safety, especially for complex urban streets and search-and-rescue subterranean environments where high-accurate GPS is not available. However current odometry estimation may introduce the drifting problems in long-term navigation without robust global localization.
Abstract: This paper proposes a real-time multi-scale semantic segmentation network (MsNet). MsNet is a combination of our novel multi-scale fusion with matching attention model (MFMA) as the decoding network and the network searched by asymptotic neural architecture search (ANAS) or MobileNetV3 as the encoding network.
Abstract: Autonomous wheelchairs can address a very large need in many populations by serving as the gateway to a much higher degree of independence and mobility capability. This is due to the fact that the big picture idea for autonomous wheelchairs integration into the transportation chain is to allow for individuals to be able to utilize the Intelligent wheelchair to reach the vehicle (regardless of terrain), mount into autonomous wheelchair that navigates to desired destination, and finally autonomous wheelchair dismounts.
Abstract: The fundamental aspect of unmanned ground vehicle (UGV) navigation, especially over off-road environments, are representations of terrain describing geometry, types, and traversability. One of the typical representations of the environment is digital surface models (DSMs) which efficiently encode geometric information.
Abstract: The concrete aging problem has gained more attention in recent years as more bridges and tunnels in the United States lack proper maintenance. Though the Federal Highway Administration requires these public concrete structures to be inspected regularly, on-site manual inspection by human operators is time-consuming and labor-intensive.
Abstract: Handicapped individuals often rely heavily on various assistive technologies including wheelchairs and the purpose of these technologies is to enable greater levels of independence for the user. In the development of autonomous wheelchairs, it is imperative that the wheelchair maintains appropriate stability for the user in an outdoor urban environment.
Abstract: Driving inattention caused by drowsiness has been a significant reason for vehicle crash accidents, and there is a critical need to augment driving safety by monitoring driver drowsiness behaviors. For real-time drowsy driving awareness, we propose a vision-based driver drowsiness monitoring system (DDMS) for driver drowsiness behavior recognition and analysis.
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.