Kudan 3D-Lidar SLAM (KdLidar) in action: In a shopping center for autonomous cleaning or service robots







March 18and2022

Kudan inc.

Kudan 3D-Lidar SLAM (KdLidar) in action:

In a shopping mall for autonomous cleaning or service robots

We have been working tirelessly on several exciting autonomous mobile robot projects with Visual SLAM and 3D-Lidar SLAM, and we wanted to share a glimpse of how our SLAM works on real projects and environments.

This article presents an example of using KdLidar for autonomous mobile robots in an indoor commercial facility (Data courtesy of Squad Robotics).

Here are some of the typical challenges in commercial environments that we frequently face:

  1. Ramp: Slopes and ramps are part of the design of many commercial facilities such as shopping malls and office buildings. Ramps can be a challenge for 2D-Lidar based SLAM as it tends to get lost as the plane the laser is aimed at changes and the laser does not see the same plane as it moves up and down the ramp .
  2. Moving objects and scenery changes: As you can imagine, people and sometimes crowds are usually part of the makeup of an environment in commercial buildings, especially during the day. In addition to these dynamic environments where the scenery changes, the lighting conditions change over the course of a day, and throughout the year. However, our 3D-Lidar SLAM algorithm is robust against these harsh conditions, and combined with the wider FoV and precise depth sensing capabilities of 3D-Lidar, allows us to overcome these challenges.
  3. The night: Cleaning robots or security robots are supposed to work during the night as well. Obviously, vision-based approaches struggle in dim or dim environments. 3D-Lidar SLAM does not.
  4. Initial start: Some 2D/3D SLAM Lidars require an initial indication of where the robot is, in order for the system to understand where it is when the robot is activated. Sometimes when a robot loses tracking, an operator must manually move the robot to known locations to reset the position. Kudan’s 3D-Lidar SLAM is able to locate its position automatically and precisely without any external intervention.

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That being said, here is a demo of KdLidar working in a mall. KdLidar in Action: SLAM in a shopping center for autonomous cleaning or service robots

As you can see, when the robot completes the first loop (at ~0’17), it triggers a loop close, and the map has been adjusted and optimized to improve its accuracy. The result is a clean, crisp, connected point cloud.

To provide more context, here are some details about the demo environment and settings.

  • Area size: 180m x 130m (or 590ft x 430ft)
  • Sensor: squeeze out OS0-16 3D lidar: lidar only, no other sensor fusion (however, we can use other sensors if needed)
  • Map generation time: 6 minutes on an Intel Core i7 processor (data collection took 44 minutes)

Although the area is relatively small and simple, KdLidar can easily scale to handle most commercial environments. We hope you’ll think of us when you need a simple and robust SLAM solution for your commercial installation!

We are excited to see many projects going well and contributing to the expansion of autonomous mobile robots around the world!

About Kudan Inc.

Kudan (Tokyo Stock Exchange Securities Code: 4425) is a high-tech research and development company specializing in artificial perception (AP) algorithms. To add to

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artificial intelligence (AI), AP functions allow machines to develop their autonomy. Currently, Kudan uses its high-level technical innovation to explore business areas based on its own established deep technology stage models that have far-reaching impact on several major industrial fields.

For more information, please visit Kudan’s website at https://www.kudan.io/.

Company details

Name: Kudan Inc.

Securities Code: 4425

Representative: CEO Daiu Ko

For more details, please contact us from here.

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Kudan inc. published this content on March 18, 2022 and is solely responsible for the information contained therein. Distributed by Public, unedited and unmodified, on March 18, 2022 06:10:07 UTC.

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