Korea Advanced Institute of Science and Technology (KAIST)
Abstract
In this paper, we present two novel approaches,
Super Rays and Culling Region, for efficiently updating gridbased
occupancy maps with point clouds. Rays, which traverse
from the sensor origin to the sensor data, update the occupancy
probabilities of a map representing an environment. Based on
the ray model, we define a super ray as a representative ray
to multiple rays having the same traversal patterns during the
map updates. Our super rays utilize the geometric information of
rays and reduce the number of points used for updating the map.
For constructing super rays efficiently, we propose mapping lines
for handling 2D and 3D cases from an observation that edges
or grid points branch out the traversal patterns on the map.
Furthermore, we introduce a culling region using the occupancy
states of the updated map for reducing redundant computations
occurred in updates. The super rays perform the update process
in a single traversal, and the culling region reduces the number
of unnecessary traversals for updating the map. As a result,
our combined method improves the update performance without
compromising any representation accuracy of a grid-based map.
We test the update performance of the proposed method using
public indoor and outdoor datasets. Our combined approach
shows up to 11.8 times and 2.8 times performance improvement
over the state-of-the-art update methods of grid-based maps in
the indoor and outdoor scenes, respectively. Also, we compare
the update speed and the representation accuracy of our method
using KITTI dataset over the state-of-the-art learning based
occupancy maps. In a navigation scenario that raw point clouds
are acquired in 10 Hz, our method shows the best performance
on the update speed and thus the highest representation accuracy
within a given time.
This figure represents an overview of our super ray
when we have the new measurements as shown in (a).
(b) and (c) represent occupancy
probabilities of cells after updating the 2D grid map with different methods.
The green and red cells have free and occupied states, respectively. The bold
numbers with * notation in cells indicate that those cells are classified into
fully occupied or fully free state.
In (b), the state-of-the-art method updates the same set of cells for three different rays.
The blue ray in (c) is a super ray computed out of those three rays in (b).
The super ray updates the map with a single traversal on the cells.
This figure shows an overview of our culling region, given the new
measurements as shown in (a). In (b), the prior method causes redundant
computation on traversals on the cells having fully-free states. The blue box
in (c) is a culling region that prevents the three rays to traverse the fully-free
cells for updates. In this figure, we use the same setting with the above figure.
These figures visualize the points that each map classifies the test points to be occupied
in our navigation scenario. We do not visualize the free points
in this figure to avoid cluttered visualization, but consider them to compute the representation accuracy.
The color represents the relative height of points, and
the number in parenthesis is the representation accuracy and the update speed of a map.
Our method shows the fastest update performance resulting in the
highest representation accuracy.
@article{kwon2019super,
title={Super rays and culling region for real-time updates on grid-based occupancy maps},
author={Kwon, Youngsun and Kim, Donghyuk and An, Inkyu and Yoon, Sung-eui},
journal={IEEE Transactions on Robotics},
volume={35},
number={2},
pages={482--497},
year={2019},
publisher={IEEE}
}