4 research outputs found
Heterogeneous Multi-sensor Calibration based on Graph Optimization
Many robotics and mapping systems contain multiple sensors to perceive the
environment. Extrinsic parameter calibration, the identification of the
position and rotation transform between the frames of the different sensors, is
critical to fuse data from different sensors. When obtaining multiple camera to
camera, lidar to camera and lidar to lidar calibration results, inconsistencies
are likely. We propose a graph-based method to refine the relative poses of the
different sensors. We demonstrate our approach using our mapping robot
platform, which features twelve sensors that are to be calibrated. The
experimental results confirm that the proposed algorithm yields great
performance
Furniture Free Mapping using 3D Lidars
Mobile robots depend on maps for localization, planning, and other
applications. In indoor scenarios, there is often lots of clutter present, such
as chairs, tables, other furniture, or plants. While mapping this clutter is
important for certain applications, for example navigation, maps that represent
just the immobile parts of the environment, i.e. walls, are needed for other
applications, like room segmentation or long-term localization. In literature,
approaches can be found that use a complete point cloud to remove the furniture
in the room and generate a furniture free map. In contrast, we propose a
Simultaneous Localization And Mapping (SLAM)-based mobile laser scanning
solution. The robot uses an orthogonal pair of Lidars. The horizontal scanner
aims to estimate the robot position, whereas the vertical scanner generates the
furniture free map. There are three steps in our method: point cloud
rearrangement, wall plane detection and semantic labeling. In the experiment,
we evaluate the efficiency of removing furniture in a typical indoor
environment. We get precision in keeping the wall in the 3D result,
which shows that our algorithm can remove most of the furniture in the
environment. Furthermore, we introduce the application of 2D furniture free
mapping for room segmentation
Fast 2D Map Matching Based on Area Graphs
We present a novel area matching algorithm for merging two different 2D grid
maps. There are many approaches to address this problem, nevertheless, most
previous work is built on some assumptions, such as rigid transformation, or
similar scale and modalities of two maps. In this work we propose a 2D map
matching algorithm based on area segmentation. We transfer general 2D occupancy
grid maps to an area graph representation, then compute the correct results by
voting in that space. In the experiments, we compare with a state-of-the-art
method applied to the matching of sensor maps with ground truth layout maps.
The experiment shows that our algorithm has a better performance on large-scale
maps and a faster computation speed.Comment: 8 pages, 42 figures, accepted by Robio 201
Advanced Mapping Robot and High-Resolution Dataset
This paper presents a fully hardware synchronized mapping robot with support
for a hardware synchronized external tracking system, for super-precise timing
and localization. Nine high-resolution cameras and two 32-beam 3D Lidars were
used along with a professional, static 3D scanner for ground truth map
collection. With all the sensors calibrated on the mapping robot, three
datasets are collected to evaluate the performance of mapping algorithms within
a room and between rooms. Based on these datasets we generate maps and
trajectory data, which is then fed into evaluation algorithms. We provide the
datasets for download and the mapping and evaluation procedures are made in a
very easily reproducible manner for maximum comparability. We have also
conducted a survey on available robotics-related datasets and compiled a big
table with those datasets and a number of properties of them.Comment: arXiv admin note: substantial text overlap with arXiv:1905.0948