15,358 research outputs found
3D Reconstruction & Assessment Framework based on affordable 2D Lidar
Lidar is extensively used in the industry and mass-market. Due to its
measurement accuracy and insensitivity to illumination compared to cameras, It
is applied onto a broad range of applications, like geodetic engineering, self
driving cars or virtual reality. But the 3D Lidar with multi-beam is very
expensive, and the massive measurements data can not be fully leveraged on some
constrained platforms. The purpose of this paper is to explore the possibility
of using cheap 2D Lidar off-the-shelf, to preform complex 3D Reconstruction,
moreover, the generated 3D map quality is evaluated by our proposed metrics at
the end. The 3D map is constructed in two ways, one way in which the scan is
performed at known positions with an external rotary axis at another plane. The
other way, in which the 2D Lidar for mapping and another 2D Lidar for
localization are placed on a trolley, the trolley is pushed on the ground
arbitrarily. The generated maps by different approaches are converted to
octomaps uniformly before the evaluation. The similarity and difference between
two maps will be evaluated by the proposed metrics thoroughly. The whole
mapping system is composed of several modular components. A 3D bracket was made
for assembling of the Lidar with a long range, the driver and the motor
together. A cover platform made for the IMU and 2D Lidar with a shorter range
but high accuracy. The software is stacked up in different ROS packages.Comment: 7 pages, 9 Postscript figures. Accepted by 2018 IEEE International
Conference on Advanced Intelligent Mechatronic
A Joint 3D-2D based Method for Free Space Detection on Roads
In this paper, we address the problem of road segmentation and free space
detection in the context of autonomous driving. Traditional methods either use
3-dimensional (3D) cues such as point clouds obtained from LIDAR, RADAR or
stereo cameras or 2-dimensional (2D) cues such as lane markings, road
boundaries and object detection. Typical 3D point clouds do not have enough
resolution to detect fine differences in heights such as between road and
pavement. Image based 2D cues fail when encountering uneven road textures such
as due to shadows, potholes, lane markings or road restoration. We propose a
novel free road space detection technique combining both 2D and 3D cues. In
particular, we use CNN based road segmentation from 2D images and plane/box
fitting on sparse depth data obtained from SLAM as priors to formulate an
energy minimization using conditional random field (CRF), for road pixels
classification. While the CNN learns the road texture and is unaffected by
depth boundaries, the 3D information helps in overcoming texture based
classification failures. Finally, we use the obtained road segmentation with
the 3D depth data from monocular SLAM to detect the free space for the
navigation purposes. Our experiments on KITTI odometry dataset, Camvid dataset,
as well as videos captured by us, validate the superiority of the proposed
approach over the state of the art.Comment: Accepted for publication at IEEE WACV 201
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