ALTO: A Large-Scale Dataset for UAV Visual Place Recognition and Localization

• URL: http://arxiv.org/abs/2207.12317v1
• Date: Tue, 19 Jul 2022 21:13:44 GMT
• Title: ALTO: A Large-Scale Dataset for UAV Visual Place Recognition and Localization
• Authors: Ivan Cisneros, Peng Yin, Ji Zhang, Howie Choset and Sebastian Scherer
• Abstract summary: The ALTO dataset is a vision-focused dataset for the development and benchmarking of Visual Place Recognition methods for Unmanned Aerial Vehicles. The dataset is composed of two long (approximately 150km and 260km) trajectories flown by a helicopter over Ohio and Pennsylvania.
• Score: 22.992887167994766
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present the ALTO dataset, a vision-focused dataset for the development and benchmarking of Visual Place Recognition and Localization methods for Unmanned Aerial Vehicles. The dataset is composed of two long (approximately 150km and 260km) trajectories flown by a helicopter over Ohio and Pennsylvania, and it includes high precision GPS-INS ground truth location data, high precision accelerometer readings, laser altimeter readings, and RGB downward facing camera imagery. In addition, we provide reference imagery over the flight paths, which makes this dataset suitable for VPR benchmarking and other tasks common in Localization, such as image registration and visual odometry. To the author's knowledge, this is the largest real-world aerial-vehicle dataset of this kind. Our dataset is available at https://github.com/MetaSLAM/ALTO.

Related papers

• Game4Loc: A UAV Geo-Localization Benchmark from Game Data [0.0]
  We introduce a more practical UAV geo-localization task including partial matches of cross-view paired data. Experiments demonstrate the effectiveness of our data and training method for UAV geo-localization.
  arXiv  Detail & Related papers  (2024-09-25T13:33:28Z)
• UAV-VisLoc: A Large-scale Dataset for UAV Visual Localization [20.37586403749362]
  We present a large-scale dataset, UAV-VisLoc, to facilitate the UAV visual localization task. Our dataset includes 6,742 drone images and 11 satellite maps, with metadata such as latitude, longitude, altitude, and capture date.
  arXiv  Detail & Related papers  (2024-05-20T10:24:10Z)
• Multiview Aerial Visual Recognition (MAVREC): Can Multi-view Improve Aerial Visual Perception? [57.77643186237265]
  We present Multiview Aerial Visual RECognition or MAVREC, a video dataset where we record synchronized scenes from different perspectives. MAVREC consists of around 2.5 hours of industry-standard 2.7K resolution video sequences, more than 0.5 million frames, and 1.1 million annotated bounding boxes. This makes MAVREC the largest ground and aerial-view dataset, and the fourth largest among all drone-based datasets.
  arXiv  Detail & Related papers  (2023-12-07T18:59:14Z)
• A Large Scale Homography Benchmark [52.55694707744518]
  We present a large-scale dataset of Planes in 3D, Pi3D, of roughly 1000 planes observed in 10 000 images from the 1DSfM dataset. We also present HEB, a large-scale homography estimation benchmark leveraging Pi3D.
  arXiv  Detail & Related papers  (2023-02-20T14:18:09Z)
• HPointLoc: Point-based Indoor Place Recognition using Synthetic RGB-D Images [58.720142291102135]
  We present a novel dataset named as HPointLoc, specially designed for exploring capabilities of visual place recognition in indoor environment. The dataset is based on the popular Habitat simulator, in which it is possible to generate indoor scenes using both own sensor data and open datasets.
  arXiv  Detail & Related papers  (2022-12-30T12:20:56Z)
• VPAIR -- Aerial Visual Place Recognition and Localization in Large-scale Outdoor Environments [49.82314641876602]
  We present a new dataset named VPAIR. The dataset was recorded on board a light aircraft flying at an altitude of more than 300 meters above ground. The dataset covers a more than one hundred kilometers long trajectory over various types of challenging landscapes.
  arXiv  Detail & Related papers  (2022-05-23T18:50:08Z)
• A benchmark dataset for deep learning-based airplane detection: HRPlanes [3.5297361401370044]
  We create a novel airplane detection dataset called High Resolution Planes (HRPlanes) by using images from Google Earth (GE) HRPlanes include GE images of several different airports across the world to represent a variety of landscape, seasonal and satellite geometry conditions obtained from different satellites. Our preliminary results show that the proposed dataset can be a valuable data source and benchmark data set for future applications.
  arXiv  Detail & Related papers  (2022-04-22T23:49:44Z)
• OpenREALM: Real-time Mapping for Unmanned Aerial Vehicles [62.997667081978825]
  OpenREALM is a real-time mapping framework for Unmanned Aerial Vehicles (UAVs) Different modes of operation allow OpenREALM to perform simple stitching assuming an approximate plane ground. In all modes incremental progress of the resulting map can be viewed live by an operator on the ground.
  arXiv  Detail & Related papers  (2020-09-22T12:28:14Z)
• Perceiving Traffic from Aerial Images [86.994032967469]
  We propose an object detection method called Butterfly Detector that is tailored to detect objects in aerial images. We evaluate our Butterfly Detector on two publicly available UAV datasets (UAVDT and VisDrone 2019) and show that it outperforms previous state-of-the-art methods while remaining real-time.
  arXiv  Detail & Related papers  (2020-09-16T11:37:43Z)
• Vehicle Position Estimation with Aerial Imagery from Unmanned Aerial Vehicles [4.555256739812733]
  This work describes a process to estimate a precise vehicle position from aerial imagery. The state-of-the-art deep neural network Mask-RCNN is applied for that purpose. A mean accuracy of 20 cm can be achieved with flight altitudes up to 100 m, Full-HD resolution and a frame-by-frame detection.
  arXiv  Detail & Related papers  (2020-04-17T12:29:40Z)
• AU-AIR: A Multi-modal Unmanned Aerial Vehicle Dataset for Low Altitude Traffic Surveillance [20.318367304051176]
  Unmanned aerial vehicles (UAVs) with mounted cameras have the advantage of capturing aerial (bird-view) images. Several aerial datasets have been introduced, including visual data with object annotations. We propose a multi-purpose aerial dataset (AU-AIR) that has multi-modal sensor data collected in real-world outdoor environments.
  arXiv  Detail & Related papers  (2020-01-31T09:45:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.