Results 21 to 30 of about 852,520 (337)
F-LOAM : Fast LiDAR Odometry and Mapping [PDF]
IEEE/RJS International Conference on Intelligent RObots and Systems, 2021 Simultaneous Localization and Mapping (SLAM) has wide robotic applications such as autonomous driving and unmanned aerial vehicles. Both computational efficiency and localization accuracy are of great importance towards a good SLAM system. Existing works
Han Wang +3 more
semanticscholar +1 more source
LiDAR odometry survey: recent advancements and remaining challenges [PDF]
Intelligent Service Robotics, 2023 Odometry is crucial for robot navigation, particularly in situations where global positioning methods like global positioning system are unavailable. The main goal of odometry is to predict the robot’s motion and accurately determine its current location.
Dongjae Lee +3 more
semanticscholar +1 more source
Point Density-Aware Voxels for LiDAR 3D Object Detection [PDF]
Computer Vision and Pattern Recognition, 2022 LiDAR has become one of the primary 3D object detection sensors in autonomous driving. However, LiDAR's diverging point pattern with increasing distance results in a non-uniform sampled point cloud ill-suited to discretized volumetric feature extraction.
Jordan S. K. Hu +2 more
semanticscholar +1 more source
NeRF-LiDAR: Generating Realistic LiDAR Point Clouds with Neural Radiance Fields [PDF]
AAAI Conference on Artificial Intelligence, 2023 Labelling LiDAR point clouds for training autonomous driving is extremely expensive and difficult. LiDAR simulation aims at generating realistic LiDAR data with labels for training and verifying self-driving algorithms more efficiently.
Junge Zhang +3 more
semanticscholar +1 more source
Micromachines, 2023 Atmospheric particulate pollution poses a great danger to the environment and human health, and there is a strong need to develop equipment for collecting and separating particulate matter of different particle sizes to study the effects of particulate ...
Ruofei Wang +3 more
doaj +1 more source
SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences
IEEE International Conference on Computer Vision, 2019 Semantic scene understanding is important for various applications. In particular, self-driving cars need a fine-grained understanding of the surfaces and objects in their vicinity.
Jens Behley +6 more
semanticscholar +1 more source
Is Pseudo-Lidar needed for Monocular 3D Object detection? [PDF]
IEEE International Conference on Computer Vision, 2021 Recent progress in 3D object detection from single images leverages monocular depth estimation as a way to produce 3D pointclouds, turning cameras into pseudo-lidar sensors.
Dennis Park +4 more
semanticscholar +1 more source
RUNNING TO SAFETY: ANALYSIS OF DISASTER SUSCEPTIBILITY OF NEIGHBORHOODS AND PROXIMITY OF SAFETY FACILITIES IN SILAY CITY, PHILIPPINES [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2016 Going on foot is the most viable option when emergency responders fail to show up in disaster zones at the quickest and most reasonable time. In the Philippines, the efficacy of disaster management offices is hampered by factors such as, but not limited ...
C. L. Patiño +3 more
doaj +1 more source
R3LIVE: A Robust, Real-time, RGB-colored, LiDAR-Inertial-Visual tightly-coupled state Estimation and mapping package [PDF]
IEEE International Conference on Robotics and Automation, 2021 In this paper, we propose a novel LiDAR-Inertial-Visual sensor fusion framework termed R3LIVE, which takes advantage of measurement of LiDAR, inertial, and visual sensors to achieve robust and accurate state estimation. R3LIVE consists of two subsystems,
Jiarong Lin, Fu Zhang
semanticscholar +1 more source
URBAN TREE CLASSIFICATION USING FULL-WAVEFORM AIRBORNE LASER SCANNING [PDF]
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2016 Vegetation mapping in urban environments plays an important role in biological research and urban management. Airborne laser scanning provides detailed 3D geodata, which allows to classify single trees into different taxa.
Zs. Koma, K. Koenig, B. Höfle
doaj +1 more source