1,746 research outputs found
Instant Object Detection in Lidar Point Clouds
In this paper we present a new approach for object classification in continuously streamed Lidar point clouds collected from urban areas. The input of our framework is raw 3-D point cloud sequences captured by a Velodyne HDL-64 Lidar, and we aim to extract all vehicles and pedestrians in the neighborhood of the moving sensor. We propose a complete pipeline developed especially for distinguishing outdoor 3-D urban objects.
Firstly, we segment the point cloud into regions of ground, short objects (i.e. low foreground) and tall objects (high foreground). Then using our novel two-layer grid structure, we perform efficient connected component analysis on the foreground regions, for producing distinct groups of points which represent different urban objects. Next, we create depth-images from the object candidates, and apply an appearance based preliminary classification by a Convolutional Neural Network (CNN). Finally we refine the classification with contextual features considering the possible expected scene topologies. We tested our algorithm in real Lidar measurements, containing 1159 objects captured from different urban scenarios
Weighted simplicial complex reconstruction from mobile laser scanning using sensor topology
We propose a new method for the reconstruction of simplicial complexes
(combining points, edges and triangles) from 3D point clouds from Mobile Laser
Scanning (MLS). Our method uses the inherent topology of the MLS sensor to
define a spatial adjacency relationship between points. We then investigate
each possible connexion between adjacent points, weighted according to its
distance to the sensor, and filter them by searching collinear structures in
the scene, or structures perpendicular to the laser beams. Next, we create and
filter triangles for each triplet of self-connected edges and according to
their local planarity. We compare our results to an unweighted simplicial
complex reconstruction.Comment: 8 pages, 11 figures, CFPT 2018. arXiv admin note: substantial text
overlap with arXiv:1802.0748
Using Lidar Intensity for Robot Navigation
We present Multi-Layer Intensity Map, a novel 3D object representation for
robot perception and autonomous navigation. Intensity maps consist of multiple
stacked layers of 2D grid maps each derived from reflected point cloud
intensities corresponding to a certain height interval. The different layers of
intensity maps can be used to simultaneously estimate obstacles' height,
solidity/density, and opacity. We demonstrate that intensity maps' can help
accurately differentiate obstacles that are safe to navigate through (e.g.
beaded/string curtains, pliable tall grass), from ones that must be avoided
(e.g. transparent surfaces such as glass walls, bushes, trees, etc.) in indoor
and outdoor environments. Further, to handle narrow passages, and navigate
through non-solid obstacles in dense environments, we propose an approach to
adaptively inflate or enlarge the obstacles detected on intensity maps based on
their solidity, and the robot's preferred velocity direction. We demonstrate
these improved navigation capabilities in real-world narrow, dense environments
using a real Turtlebot and Boston Dynamics Spot robots. We observe significant
increases in success rates to more than 50%, up to a 9.5% decrease in
normalized trajectory length, and up to a 22.6% increase in the F-score
compared to current navigation methods using other sensor modalities.Comment: 9 pages, 7 figure
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