Fox News Flash top headlines are here. Check out whats clicking on Foxnews.com. One of the biggest hurdles in developing humanoid robots is the sheer amount of training data required. Teaching machines to act like humans demands massive video datasets. Collecting that data is expensive, time-consuming and difficult to scale.

Abstract--In the field of multi-sensor fusion for simultaneous localization and mapping (SLAM), monocular cameras and IMU s are widely used to build simple and effective visual-inerti al systems. However, limited research has explored the integr ation of motor-encoder devices to enhance SLAM performance. By incorporating such devices, it is possible to significantly improve active capability and field of view (FOV) with minimal additi onal cost and structural complexity. This paper proposes a novel visual-inertial-encoder tightly coupled odometry (VIEO) based on a ViDAR (Video Detection and Ranging) device. A ViDAR calibration method is introduced to ensure accurate initia lization for VIEO. In addition, a platform motion decoupled active SLAM method based on deep reinforcement learning (DRL) is proposed. Experimental data demonstrate that the proposed Vi-DAR and the VIEO algorithm significantly increase cross-fra me co-visibility relationships compared to its correspondin g visual-inertial odometry (VIO) algorithm, improving state estima tion accuracy. The proposed methodolog y sheds fresh insights into both the updated platform design and decoupled approach of active SLAM systems in complex environments. N recent years, visual odometry (VO) and visual-inertial odometry (VIO) have made significant advancements. This work was supported in part by the Beijing Natural Scienc e Foundation under Grant 2022MQ05, in part by the CIE-Tencent Robotics X R hino-Bird Focused Research Program under Grant 2022-07, and in part by the National Natural Science Foundation of China under Grant 62203015, G rant 62303020, Grant 62303021, and Grant 62273351. Zhanhua Xin, Zhihao Wang, Shihan Kong, Y an Xiong, and Junzhi Y u are with the State Key Laboratory for Turbulence and Complex Systems, Department of Advanced Manufacturing and Robotics, C ollege of Engineering, Peking University, Beijing 100871, China (email: xinzhan-hua@stu.pku.edu.cn;

Sentient Vision Systems is an artificial intelligence (AI) company that uses advanced software to enhance the performance of sensors and mission systems. ViDAR (for Visual Detection and Ranging) can detect a target in the imagery feed, discriminate between possible alternatives, and draw the operator's eye to what he or she is looking for. The power of AI can differentiate, from a distance of five nautical miles, between an arctic ice floe, a breaking wave and an upturned boat. AI and mastery of traditional computer vision technology underpins everything that Sentient Vision Systems has done over the past 17 years, since it started working on target detection solutions over land and maritime environments. Sentient's ViDAR systems use the AI within its deep learning and computer vision algorithms to detect tiny targets that are almost invisible in the imagery feed from an EO/IR sensor, especially in very challenging conditions, and filter out irrelevant information.

During a workshop on autonomous driving at the Conference on Computer Vision and Pattern Recognition (CVPR) 2020, Waymo and Uber presented research to improve the reliability -- and safety -- of their self-driving systems. Raquel Urtasun, chief scientist at Uber's Advanced Technologies Group, demonstrated a pair of technologies that leverage vehicle-to-vehicle communication for navigation, traffic modeling, and more. It learns 3D geometry from image sequences -- i.e., frames captured by car-mounted cameras -- by exploiting motion parallax, a change in position caused by movement. Given a pair of images and lidar data, ViDAR can predict future camera viewpoints and depth data. According to Anguelov, ViDAR uses shutter timings to account for rolling shutter, the camera capture method in which not all parts of a scene are recorded simultaneously. Along with support for up to five cameras, this mitigating step enables the framework to avoid displacements at higher speeds while improving accuracy.