Search

We present a review of 3D point cloud processing and learning for autonomous driving. As one of the most important sensors in autonomous vehicles (AVs), lidar sensors collect 3D point clouds that precisely record the external surfaces of objects and scenes. The tools for 3D point cloud processing and learning are critical to the map creation, localization, and perception modules in an AV. Although much attention has been paid to data collected from cameras, such as images and videos, an increasing number of researchers have recognized the importance and significance of lidar in autonomous driving and have proposed processing and learning algorithms that exploit 3D point clouds. We review the recent progress in this research area and summarize what has been tried and what is needed for practical and safe AVs. We also offer perspectives on open issues that are needed to be solved in the future. [ABSTRACT FROM AUTHOR]

Person detection from vehicles hasmade rapid progress recentlywith the advent ofmultiple highquality datasets of urban and highway driving, yet no large-scale benchmark is available for the same problem in off-road or agricultural environments. Here we present the National Robotics EngineeringCenter (NREC)Agricultural Person-DetectionDataset to spur research in these environments. It consists of labeled stereo video of people in orange and apple orchards taken from two perception platforms (a tractor and a pickup truck), along with vehicle position data from Real Time Kinetic (RTK) GPS. We define a benchmark on part of the dataset that combines a total of 76k labeled person images and 19k sampled person-free images. The dataset highlights several key challenges of the domain, including varying environment, substantial occlusion by vegetation, people in motion and in nonstandard poses, and people seen from a variety of distances; metadata are included to allow targeted evaluation of each of these effects. Finally,we present baseline detection performance results for three leading approaches from urban pedestrian detection and our own convolutional neural network approach that benefits from the incorporation of additional image context.We show that the success of existing approaches on urban data does not transfer directly to this domain. [ABSTRACT FROM AUTHOR]

Occupational noise exposure is a recognized hazard for employees working near equipment and processes that generate high levels of sound pressure. High sound pressure levels have the potential to result in temporary or permanent alteration in hearing perception. The cleaning of cages used to house laboratory research animals is a process that uses equipment capable of generating high sound pressure levels. The purpose of this research study was to assess occupational exposure to sound pressure levels for employees operating cage decontamination equipment. This study reveals the potential for overexposure to hazardous noise as defined by the Occupational Safety and Health Administration (OSHA) permissible exposure limit and consistent surpassing of the OSHA action level. These results emphasize the importance of evaluating equipment and room design when acquiring new cage decontamination equipment in order to minimize employee exposure to potentially hazardous noise pressure levels. [ABSTRACT FROM AUTHOR]

This paper describes the key system components of a team of three tractors in a peat moss harvesting operation. The behavior and actions of the tractors were designed to mimic manual harvest operations while maintaining a safe operating environment. To accomplish this objective, each of the three tractors was equipped with a bolt-on automation package, and a human operator (team leader) was given a remote user interface to command and monitor the mission. The automation package included positioning, planning, and control, as well as coordination and perception systems to preserve field harvesting order, detect obstacles, and report physical changes in the operating environment. The system performed more than 100 test field harvesting missions during one season in a working peat bog, including three complete system tests with the end users. © 2009 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]