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.
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.
Abstract: We present a novel collaborative mapping and autonomous parking system for semi-structured multi-story parking garages, based on cooperative 3-D LiDAR point cloud registration and Bayesian probabilistic updating. First, an inertial-enhanced (IE) generalized iterative closest point (G-ICP) approach is presented to perform high accuracy registration for LiDAR odometry, which is loosely coupled with inertial measurement unit using multi-state extended Kalman filter fusion.
Abstract: Concrete spalling and crack inspection is a labor intensive and routine task. However, it plays an important role in structure health monitoring (SHM) of civil infrastructures. Autonomous inspection with robots has been regarded as one of the best ways to reduce both error and cost.
Abstract: This paper presents an innovative wearable system to assist visually impaired people navigate indoors in real time. Our proposed system incorporates state-of-the-art handheld devices from Google’s Project Tango and integrates path planner and obstacle avoidance submodules, as well as human-computer interaction techniques, to provide assistance to the user.
Abstract: Scene text in indoor environments usually preserves and communicates important contextual information which can significantly enhance the independent travel of blind and visually impaired people. In this paper, we present an assistive text spotting navigation system based on an RGB-D mobile device for blind or severely visually impaired people.
Abstract: This paper presents a novel mobile wearable context-aware indoor maps and navigation system with obstacle avoidance for the blind. The system includes an indoor map editor and an App on Tango devices with multiple modules.