Results 31 to 40 of about 14,971 (201)
Research on SLAM Algorithm of Mobile Robot Based on the Fusion of 2D LiDAR and Depth Camera
IEEE Access, 2020 This paper proposes a new Simultaneous Localization and Mapping (SLAM) method on the basis of graph-based optimization through the combination of the Light Detection and Ranging (LiDAR), RGB-D camera, encoder and Inertial Measurement Unit (IMU).
Lili Mu +5 more
doaj +1 more source
An Effective Multi-Cue Positioning System for Agricultural Robotics [PDF]
, 2018 The self-localization capability is a crucial component for Unmanned Ground Vehicles (UGV) in farming applications. Approaches based solely on visual cues or on low-cost GPS are easily prone to fail in such scenarios.
Grisetti, Giorgio +4 more
core +2 more sources
VIRAL SLAM: Tightly Coupled Camera-IMU-UWB-Lidar SLAM
, 2021 In this paper, we propose a tightly-coupled, multi-modal simultaneous localization and mapping (SLAM) framework, integrating an extensive set of sensors: IMU, cameras, multiple lidars, and Ultra-wideband (UWB) range measurements, hence referred to as VIRAL (visual-inertial-ranging-lidar) SLAM.
Nguyen, Thien-Minh +4 more
openaire +2 more sources
Deep Learning-Aided Inertial/Visual/LiDAR Integration for GNSS-Challenging Environments
Sensors, 2023 This research develops an integrated navigation system, which fuses the measurements of the inertial measurement unit (IMU), LiDAR, and monocular camera using an extended Kalman filter (EKF) to provide accurate positioning during prolonged GNSS signal ...
Nader Abdelaziz, Ahmed El-Rabbany
doaj +1 more source
Consistent ICP for the registration of sparse and inhomogeneous point clouds [PDF]
, 2016 In this paper, we derive a novel iterative closest point (ICP) technique that performs point cloud alignment in a robust and consistent way. Traditional ICP techniques minimize the point-to-point distances, which are successful when point clouds contain ...
Goeman, Werner +3 more
core +1 more source
Fast and robust 3D feature extraction from sparse point clouds [PDF]
, 2016 Matching 3D point clouds, a critical operation in map building and localization, is difficult with Velodyne-type sensors due to the sparse and non-uniform point clouds that they produce.
GRISETTI, GIORGIO +2 more
core +1 more source
Scan matching by cross-correlation and differential evolution [PDF]
, 2019 Scan matching is an important task, solved in the context of many high-level problems including pose estimation, indoor localization, simultaneous localization and mapping and others.
Konečný, Jaromír +3 more
core +1 more source
RadarSLAM: Radar based Large-Scale SLAM in All Weathers
, 2020 Numerous Simultaneous Localization and Mapping (SLAM) algorithms have been presented in last decade using different sensor modalities. However, robust SLAM in extreme weather conditions is still an open research problem.
Hong, Ziyang, Petillot, Yvan, Wang, Sen
core +1 more source
Towards online mobile mapping using inhomogeneous lidar data [PDF]
, 2016 In this paper we present a novel approach to quickly obtain detailed 3D reconstructions of large scale environments. The method is based on the consecutive registration of 3D point clouds generated by modern lidar scanners such as the Velodyne HDL-32e or
Goeman, Werner +4 more
core +1 more source
A Review of Simultaneous Localization and Mapping Algorithms Based on Lidar
World Electric Vehicle JournalSimultaneous localization and mapping (SLAM) is one of the key technologies for mobile robots to achieve autonomous driving, and the lidar SLAM algorithm is the mainstream research scheme.
Yong Li +6 more
doaj +1 more source