Suma++: Efficient lidar-based semantic slam
Reliable and accurate localization and map** are key components of most autonomous
systems. Besides geometric information about the mapped environment, the semantics …
systems. Besides geometric information about the mapped environment, the semantics …
FuseSeg: Semantic segmentation of urban scenes based on RGB and thermal data fusion
Semantic segmentation of urban scenes is an essential component in various applications
of autonomous driving. It makes great progress with the rise of deep learning technologies …
of autonomous driving. It makes great progress with the rise of deep learning technologies …
Ndt-transformer: Large-scale 3d point cloud localisation using the normal distribution transform representation
3D point cloud-based place recognition is highly demanded by autonomous driving in GPS-
challenged environments and serves as an essential component (ie loop-closure detection) …
challenged environments and serves as an essential component (ie loop-closure detection) …
SA-LOAM: Semantic-aided LiDAR SLAM with loop closure
LiDAR-based SLAM system is admittedly more accurate and stable than others, while its
loop closure detection is still an open issue. With the development of 3D semantic …
loop closure detection is still an open issue. With the development of 3D semantic …
Lidar-level localization with radar? the cfear approach to accurate, fast, and robust large-scale radar odometry in diverse environments
This article presents an accurate, highly efficient, and learning-free method for large-scale
odometry estimation using spinning radar, empirically found to generalize well across very …
odometry estimation using spinning radar, empirically found to generalize well across very …
Point cloud registration algorithm based on curvature feature similarity
In this paper, an improved iterative closest point (ICP) algorithm based on the curvature
feature similarity of the point cloud is proposed to improve the performance of classic ICP …
feature similarity of the point cloud is proposed to improve the performance of classic ICP …
A novel weakly-supervised approach for RGB-D-based nuclear waste object detection
This paper addresses the problem of RGBD-based detection and categorization of waste
objects for nuclear decommissioning. To enable autonomous robotic manipulation for …
objects for nuclear decommissioning. To enable autonomous robotic manipulation for …
Recurrent-octomap: Learning state-based map refinement for long-term semantic map** with 3-d-lidar data
This letter presents a novel semantic map** approach, Recurrent-OctoMap, learned from
long-term three-dimensional (3-D) Lidar data. Most existing semantic map** approaches …
long-term three-dimensional (3-D) Lidar data. Most existing semantic map** approaches …
Design and development of software stack of an autonomous vehicle using robot operating system
In recent research activities, autonomous vehicles and self-driving technology have gained
lot of attention among scientists. The idea of autonomous vehicles can be anticipated in the …
lot of attention among scientists. The idea of autonomous vehicles can be anticipated in the …
Integrate point-cloud segmentation with 3D LiDAR scan-matching for mobile robot localization and map**
X Li, S Du, G Li, H Li - Sensors, 2019 - mdpi.com
Localization and map** are key requirements for autonomous mobile systems to perform
navigation and interaction tasks. Iterative Closest Point (ICP) is widely applied for LiDAR …
navigation and interaction tasks. Iterative Closest Point (ICP) is widely applied for LiDAR …