Ford Campus vision and lidar data set

By Gaurav Pandey, James R. McBride, Ryan M. Eustice. Int. J. Robotics Res., 2011

395 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Automotive Engineering, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/81d5294e093ca79c27b15aa08275d729118b83ce

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Citations in the corpus

MulRan: Multimodal Range Dataset for Urban Place Recognition (2020, neutral): “Datasets Freiburg [3] Ford Campus [4] KITTI [5] NCLT [6] Complex Urban [7] nuScenes [8] Marulan [9] Oxford Radar Robotcar [10] Ours (MulRan)”
Simultaneous Localization and Mapping: A Survey of Current Trends in Autonomous Driving (2017, neutral): “Ford Campus [104] 2011 √ √ √ 1 3D 64-layer (up to 120 m): 360°/0.”
Argoverse: 3D Tracking and Forecasting With Rich Maps (2019, background): “Several datasets provide raw data from self driving car sensors, but without any object annotations [27, 30, 33].”
L3-Net: Towards Learning Based LiDAR Localization for Autonomous Driving (2019, neutral): “Ford Campus[26] 5.”
A Tempt to Unify Heterogeneous Driving Databases using Traffic Primitives (2018, neutral): “After classiﬁcation and learning statistical and physical properties of primitives, the [5] png, txt, xml Camera, Lidar, GPS, IMU Ford[6] mat,ppm,log, pcap Camera, Lidar, GPS, IMU Udacity jpg, log, csv, ROSBAG Camera, Lidar, GPS,IMU Safety Pilot[11] csv Mobieye, GPS, IMU, Radar CarLab[12] csv,…”
Complex urban dataset with multi-level sensors from highly diverse urban environments (2019, methodology): “The Ford dataset (Pandey et al. 2011) contains 3D LiDAR and omnidirectional camera data in addition to data from push-bloom type 2D LiDARs suitable for 3D mapping.”
M2DP: A novel 3D point cloud descriptor and its application in loop closure detection (2016, methodology): “We evaluate the proposed method using lidar data sequences in KITTI3 [22], Freiburg Campus4 and Ford Campus5 [21].”
University of Michigan North Campus long-term vision and lidar dataset (2016, methodology): “…feature descriptors for things such as changing lighting (Carlevaris-Bianco and Eustice, 2014; Lategahn et al., 2013), weather and seasons (Krajnik et al., 2015; McManus et al., 2015), or predicting appearance change (Carlevaris-Bianco and Eustice, 2012; Linegar et al., 2015; Neubert et al., 2013).”
Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age (2016, neutral): “ images). Topic References Data IMU GPS Labels LIDAR2D LIDAR3D LOC MAP RGB RGBD Stereo Front-ends and back-ends in small / medium scale scenarios [293] X X X [319] X X X [233] X X X X X [283] X X X X [141, 239] X [134, 317, 318] X X X [122] X X X X X X X X [39] X X X X X [254] X X X X X Back-ends in”
Scan Context++: Structural Place Recognition Robust to Rotation and Lateral Variations in Urban Environments (2021, background): “Most LiDAR place recognition methods [25, 28, 32, 31] have been tested over less complex environments [12, 39] with few dynamic objects or viewpoint changes.”
A survey of deep learning techniques for autonomous driving (2019, methodology): “Ford campus vision and lidar dataset (Ford) (Pandey et al., 2011).”
Paris-Lille-3D: A large and high-quality ground-truth urban point cloud dataset for automatic segmentation and classification (2017, background): “• SLAM dataset: Ford Campus Vision and LiDAR Data Set6 (Pandey et al., 2011).”
The H3D Dataset for Full-Surround 3D Multi-Object Detection and Tracking in Crowded Urban Scenes (2019, background): “With a comparison to 2D scene understanding, relatively small efforts [16], [13], [17] have been made in 3D due to the costs for installing a high quality 3D range scanner and difficulties in labeling annotations in point cloud on a largescale.”