Results 11 to 20 of about 834,631 (325)
FAST-LIO2: Fast Direct LiDAR-Inertial Odometry [PDF]
IEEE Transactions on robotics, 2021 This article presents FAST-LIO2: a fast, robust, and versatile LiDAR-inertial odometry framework. Building on a highly efficient tightly coupled iterated Kalman filter, FAST-LIO2 has two key novelties that allow fast, robust, and accurate LiDAR ...
Wei Xu +4 more
semanticscholar +1 more source
TransFusion: Robust LiDAR-Camera Fusion for 3D Object Detection with Transformers [PDF]
Computer Vision and Pattern Recognition, 2022 LiDAR and camera are two important sensors for 3D object detection in autonomous driving. Despite the increasing popularity of sensor fusion in this field, the robustness against inferior image conditions, e.g., bad illumination and sensor misalignment ...
Xuyang Bai +6 more
semanticscholar +1 more source
BEVFusion: A Simple and Robust LiDAR-Camera Fusion Framework [PDF]
Neural Information Processing Systems, 2022 Fusing the camera and LiDAR information has become a de-facto standard for 3D object detection tasks. Current methods rely on point clouds from the LiDAR sensor as queries to leverage the feature from the image space. However, people discovered that this
Tingting Liang +8 more
semanticscholar +1 more source
Spherical Transformer for LiDAR-Based 3D Recognition [PDF]
Computer Vision and Pattern Recognition, 2023 LiDAR-based 3D point cloud recognition has benefited various applications. Without specially considering the LiDAR point distribution, most current methods suffer from information disconnection and limited receptive field, especially for the sparse ...
Xin Lai +4 more
semanticscholar +1 more source
DeepFusion: Lidar-Camera Deep Fusion for Multi-Modal 3D Object Detection [PDF]
Computer Vision and Pattern Recognition, 2022 Lidars and cameras are critical sensors that provide complementary information for 3D detection in autonomous driving. While prevalent multi-modal methods [34], [36] simply decorate raw lidar point clouds with camera features and feed them directly to ...
Yingwei Li +12 more
semanticscholar +1 more source
LVI-SAM: Tightly-coupled Lidar-Visual-Inertial Odometry via Smoothing and Mapping [PDF]
IEEE International Conference on Robotics and Automation, 2021 We propose a framework for tightly-coupled lidar-visual-inertial odometry via smoothing and mapping, LVI-SAM, that achieves real-time state estimation and map-building with high accuracy and robustness.
Tixiao Shan +3 more
semanticscholar +1 more source
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
Rethinking Range View Representation for LiDAR Segmentation [PDF]
IEEE International Conference on Computer Vision, 2023 LiDAR segmentation is crucial for autonomous driving perception. Recent trends favor point- or voxel-based methods as they often yield better performance than the traditional range view representation.
Lingdong Kong +8 more
semanticscholar +1 more source
FULLY AUTOMATED GIS-BASED INDIVIDUAL TREE CROWN DELINEATION BASED ON CURVATURE VALUES FROM A LIDAR DERIVED CANOPY HEIGHT MODEL IN A CONIFEROUS PLANTATION [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2016 The generation of high resolution canopy height model (CHM) from LiDAR makes it possible to delineate individual tree crown by means of a fully-automated method using the CHM’s curvature through its slope.
R. J. L. Argamosa +7 more
doaj +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