"Cartographer论文：基于自适应多距离扫描调度的制图SLAM优化方法"
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The paper titled "Cartographer SLAM Method for Optimization with an Adaptive Multi-Distance Scan Scheduler" is authored by Abdurahman Dwijotomo, Mohd Azizi Abdul Rahman, Mohd Hatta Mohammed Ariff, Hairi Zamzuri, and Wan Muhd Hafeez Wan Azree. The research was conducted by the Advanced Vehicle System Research Group at Universiti Teknologi Malaysia and Emoovit Technology Sdn. Bhd. The study focuses on the development of a mapping Simultaneous Localization and Mapping (SLAM) optimization method using an adaptive multi-distance scan scheduler. The authors present a novel approach to enhance the mapping capabilities of SLAM technology, aiming to improve the accuracy and efficiency of the mapping process. Through their experiments and analysis, they demonstrate the effectiveness of their method in producing high-quality maps with reduced computational effort. This research contributes to the advancement of SLAM technology in various applications, including autonomous vehicles, robotics, and augmented reality. The findings of this study have significant implications for the field of autonomous systems and pave the way for further research in SLAM optimization techniques.
Appl. Sci. 2020, 10, 347 4 of 18
the Cartographer’s local SLAM integrated with the AMDS to control the sensor distances, and the scan
matcher’s search window during map generation and pose generation.
3.1. Multi-Distance Scheduler in LiDAR Scans
A multi-distance scheduler (MDS) is inspired from the idea of multiple resolution maps in Google’s
Cartographer utilizing a multi-stage scan matcher from low to high resolution. An MDS can be used to
reduce computational cost as compared with using full high-resolution scans. It uses a scan matcher
with low-resolution map data for the initial pose estimation, which then is used to bypass scan matcher
iteration of every possible translation and rotation in a high-resolution map. Thus, it minimizes the
performance penalty while maintaining accuracy.
The multi-distance approach is similar to the multi-resolution map. Instead of controlling
resolution, we propose controlling sensor distance that generates point cloud frame (
F
), whereby
contains an array of object point (
F
) detected from the LiDAR in local sensor coordinates. Limiting
sensor distance results in different details of the scan, as shown in Figure 2.
Appl. Sci. 2020, 10, x FOR PEER REVIEW 4 of 18
to the Cartographer’s local SLAM integrated with the AMDS to control the sensor distances, and the
scan matcher’s search window during map generation and pose generation.
3.1. Multi-Distance Scheduler in LiDAR Scans
A multi-distance scheduler (MDS) is inspired from the idea of multiple resolution maps in
Google’s Cartographer utilizing a multi-stage scan matcher from low to high resolution. An MDS can
be used to reduce computational cost as compared with using full high-resolution scans. It uses a
scan matcher with low-resolution map data for the initial pose estimation, which then is used to
bypass scan matcher iteration of every possible translation and rotation in a high-resolution map.
Thus, it minimizes the performance penalty while maintaining accuracy.
The multi-distance approach is similar to the multi-resolution map. Instead of controlling
resolution, we propose controlling sensor distance that generates point cloud frame (ℱ), whereby ℱ
contains an array of object point (𝑝_𝑝𝑜𝑖𝑛𝑡
,,
) detected from the LiDAR in local sensor coordinates.
Limiting sensor distance results in different details of the scan, as shown in Figure 2.
(a) (b)
Figure 2. Sensor scan state with different distances: (a) LiDAR scan 25 m distances and (b) LiDAR
scan 60 m distances.
The controlling method is done via a scheduler, which swaps between two sensor states, namely
a short distance state and a long distance state at a fixed frequency. It is known in scan matching
algorithms that the larger the image data or map, the greater the amount of processing time required
as compared with small images [23]. However, because the large images from the sensor scan capture
more features, the computation results using these features generate less rotational and translational
errors. Thus, it is hoped that the combination of these two scan states, i.e., short and long distance,
achieves a similar result of increasing the processing speed performance while maintaining accuracy.
Some additional modifications are needed to accommodate the proposed method. Table 1 shows the
scheduler algorithm to control the distance of the scan. It uses a time variable which is based on the
period for trigger. The period ratio is configured to, for example, 1:1, 1:2, 1:3, etc. The weight of scan
matcher also needs to be weighted differently. A more extensive distance sensor scan requires higher
weight on the SLAM pose estimation as compared with a shorter scan distance because a larger map
contains more feature data, and thus is likely to have low covariance as more features are used to
compute the pose.
Figure 2.
Sensor scan state with different distances: (
a
) LiDAR scan 25 m distances and (
b
) LiDAR scan
60 m distances.
The controlling method is done via a scheduler, which swaps between two sensor states, namely
a short distance state and a long distance state at a fixed frequency. It is known in scan matching
algorithms that the larger the image data or map, the greater the amount of processing time required
as compared with small images [
23
]. However, because the large images from the sensor scan capture
more features, the computation results using these features generate less rotational and translational
errors. Thus, it is hoped that the combination of these two scan states, i.e., short and long distance,
achieves a similar result of increasing the processing speed performance while maintaining accuracy.
Some additional modifications are needed to accommodate the proposed method. Table 1 shows the
scheduler algorithm to control the distance of the scan. It uses a time variable which is based on the
period for trigger. The period ratio is configured to, for example, 1:1, 1:2, 1:3, etc. The weight of scan
matcher also needs to be weighted differently. A more extensive distance sensor scan requires higher
weight on the SLAM pose estimation as compared with a shorter scan distance because a larger map
contains more feature data, and thus is likely to have low covariance as more features are used to
compute the pose.
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