collection curated by goldbattle https://academictorrents.com/collection/state-estimation Forestry is a major industry in many parts of the world, yet this potential domain of application area has been overlooked by the robotics community. For instance, forest inventory, a cornerstone of efficient and sustainable forestry, is still traditionally performed manually by qualified professionals. The lack of automation in this particular task, consisting chiefly of measuring tree attributes, limits its speed, and therefore the area that can be economically covered. To this effect, we propose to use recent advancements in 3D mapping approaches in forests to automatically measure tree diameters from mobile robot observations. While previous studies showed the potential for such technology, they lacked a rigorous analysis of diameter estimation methods in challenging and large-scale forest environments. Here, we validated multiple diameter estimation methods, including two novel ones, in a new publicly-available dataset which includes four different forest sites, 11 trajectories, totaling 1458 tree observations and 14 000 m². From our extensive validation, we concluded that our mapping method is usable in the context of automated forest inventory, with our best diameter estimation method yielding a root mean square error of 3.45 cm for our whole dataset, and 2.04 cm in ideal conditions consisting of mature forest with well spaced trees. Furthermore, we release this dataset to the public, to spur further research in robotic forest inventories. Finally, stemming from this large-scale experiment, we provide recommendations for future deployments of mobile robots in a forestry context. https://academictorrents.com/download/cf39b5c4285f20c3539cbe9f37f6e04cfde10afa comma.ai presents comma2k19, a dataset of over 33 hours of commute in California s 280 highway. This means 2019 segments, 1 minute long each, on a 20km section of highway driving between California s San Jose and San Francisco. The dataset was collected using comma EONs that have sensors similar to those of any modern smartphone including a road-facing camera, phone GPS, thermometers and a 9-axis IMU. Additionally, the EON captures raw GNSS measurements and all CAN data sent by the car with a comma grey panda. Laika, an open-source GNSS processing library, is also introduced here. Laika produces 40% more accurate positions than the GNSS module used to collect the raw data. This dataset includes pose (position + orientation) estimates in a global reference frame of the recording camera. These poses were computed with a tightly coupled INS/GNSS/Vision optimizer that relies on data processed by Laika. comma2k19 is ideal for development and validation of tightly coupled GNSS algorithms and mapping algorithms that work with commodity sensors. https://academictorrents.com/download/65a2fbc964078aff62076ff4e103f18b951c5ddb ##About Data collection site: New College, Oxford The New College Data Set contains 30GB of data intended for use by the mobile robotics and vision research communities. Our anticipated users are parties interested in outdoor 6 D.O.F. navigation and mapping (metric or topological) using vision and/or laser. ##Data Synopsis * Gathered while traversing 2.2km through a college s grounds and adjoining parks * 5 DOF dead reckoned pose at 28Hz * Stereo imagery captured at 20Hz * 5-view omni-directional images at 5Hz * range and intensity data from two vertically mounted SICK LMS 291-S14 lasers scanning at 75Hz ##Full Data Set Here we provide the data set in full. The data set consists of three streams of information: two sets of images, one from the Stereo Camera, and one from the Ladybug Camera, and an Alog file which contains other information like laser data and odometry. The image sets come in two formats. Firstly, we provide them as single zip files (via ftp) containing all the images. These are large files due to the number of images, so the second format is the image sets broken down into ~500MB chunks (via http). Image Sets, Multiple Zip Files * (http) Zipped Stereo Images (**) * (http) Zipped Ladybug Images * (http) Zipped Panorama Images(***) Alog File * (http) Alog File (220M) Visual Odometry Data [14-06-10] Please note that this data was not originally part of the IJRR submission. * (http) Visual Odometry (VO) data (7M) (**) Please note that the stereo images contain lens distortion and are unrectified. This makes them unsuitable for direct use in, for example, local window-based stereo matching. Code is provided that will perform undisortion and rectification on these image sequences. Download details and instructions for use are given: Code to undistort and rectify stereo image sequence. (***) Panoramas are provided as a quick overview of the ladybug image data. Each image is a concatenation of the 5 camera images, all captured at the same time. We suggest using the raw ladybug images as these correspond to the files referred to in the .alog file. ##Extracting Data From The Plain-text "alog files" All non-image data is stored in a plain-text log file called an "alog". To get acquainted with this format and suitable access methods we suggest you: 1. Download some data evaluation snippets, particularly the alog which is the text file containing all non-image data, and the Flash Laser Viewer. The data set is large and users may not wish to download 30G data and start using the parsing tools immediately so we have provided some short partitions of the alog file. 2. Download and compile (as a standalone executable or from within Matlab©) the alog parsing tool we provide called uAlogParser. Full instructions on how to compile this single source file on multiple platforms are given here. 3. Try running the parser on one of the smaller evaluation alog segments. If you have access to Matlab©, try running the point cloud builder on an evaluation chunk to see some 3D laser data. 4. Download the full alog file - perhaps after finding an event that appears interesting. https://academictorrents.com/download/9e738f5ef5f1412974ab793f315450bc8da76e73 We provide a dataset collected by an autonomous ground vehicle testbed, based upon a modified Ford F-250 pickup truck. The vehicle is outfitted with a professional (Applanix POS LV) and consumer (Xsens MTI-G) Inertial Measuring Unit (IMU), a Velodyne 3D-lidar scanner, two push-broom forward looking Riegl lidars, and a Point Grey Ladybug3 omnidirectional camera system. Here we present the time-registered data from these sensors mounted on the vehicle, collected while driving the vehicle around the Ford Research campus and downtown Dearborn, Michigan during November-December 2009. The vehicle path trajectory in these datasets contain several large and small-scale loop closures, which should be useful for testing various state of the art computer vision and SLAM (Simultaneous Localization and Mapping) algorithms. The size of the dataset is huge (~100 GB) so make sure that you have sufficient bandwidth before downloading the dataset. Once downloaded you can unzip the dataset.tgz file to get the following files and folders under the main directory: Folders: SCANS, IMAGES, LCM, VELODYNE. Files: Timestamp.log, Pose-Applanix.log, Pose-Mtig.log, Gps.log, PARAM.mat Unzip the Code.tgz file. It should have two folders "C" and "MATLAB" containing the utility functions. The details about these files and folders can be found in the README file and in our IJRR data paper. Please cite the IJRR data paper when using this data set in your work. Gaurav Pandey, James R. McBride and Ryan M. Eustice, Ford campus vision and lidar data set. International Journal of Robotics Research, 30(13):1543-1552, 2011. Calibrated laser reflectivity values: This file contains the [64x256] matrix of calibrated laser reflectivity values. Each element c(j,a) of this matrix is the calibrated output when beam j observes reflectivity a. This calibrated reflectivity map of each laser of the velodyne laser scanner has been estimated by using the method given in the paper titled Unsupervised Calibration for Multi-beam Lasers by Levinson et al. The uncalibrated/observed reflectivity values are available in the LCM log files and in the pcap files of the dataset. Subset of Dataset 1 (6 GB): This contains a subset of the dataset 1. This has 200 scans starting from scan index 1000 to 1200 and the corresponding camera images. Dataset 1 (78 GB): This corresponds to a loop in the downtown Dearborn Michigan. Dataset 2 (119 GB): This corresponds to a loop inside the Ford campus in Dearborn Michigan. Files: Code.zip dataset-1-subset.tgz dataset-1.tar.gz dataset-2.tar.gz ijrr2011.pdf https://academictorrents.com/download/9aeefe49b754722eb5c051e77bacc5d75eca3ef2 Note: If you want only a subset of the data presented here (just the ROS bags, but not the extracted JPEGs and CSVs for example, since there is some duplication), you can set your torrent client to download only the files you are interested in. License: This data is licensed under the Creative Commons - Attribution 4.0 International License: . We present a novel long-term dataset of semistructured woodland terrain under varying lighting and weather conditions and with changing vegetation, infrastructure, and pedestrian traffic. This dataset is intended to aid the development of field robotics algorithms for long-term deployment in challenging outdoor environments. It includes more than 8 hours of trail navigation, with more available in the future as the environment changes. The data consist of readings from calibrated and synchronized sensors operating at 5 Hz to 50 Hz in the form of color stereo and grayscale monocular camera images, vertical and push-broom laser scans, GPS locations, wheel odometry, inertial measurements, and barometric pressure values. Each traversal covers approximately 4 km across three diverse woodland trail environments, and we have recorded under four different lighting and weather conditions to date: dry; wet; dusk; night. We also provide 383 hand-matched location correspondences between traversals as ground-truth for benchmarking place recognition and mapping algorithms. This paper describes the configuration of the vehicle, the trail environments covered, and the format of the data we provide. https://academictorrents.com/download/e3d6b8d9e87cab68c7947e800e337e58fc8d8e59