"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 (

), whereby

contains an array of object point (

) 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: (

) LiDAR scan 25 m distances and (

) 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 [

]. 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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