V-MMS (Vehicle Mobile Mapping System)

Overview

High-precision Mobile Mapping Systems (MMS) have been available for some time, but have not yet achieved widespread market adoption due to various factors. Kudan's V-MMS is an innovative product designed to overcome the challenges faced by conventional MMS.

Conventional MMS Challenges

  • High quality but expensive
    The point cloud quality is high, but the price is expensive, which creates a high barrier to easy point cloud acquisition and generation.
  • Difficulty integrating with other point clouds
    Although simple alignment is possible, point cloud acquisition is generally intended to be done with the same model of device.
  • Difficult to use and master
    It requires a certain level of expertise, and it takes time to master its use.

Concept of Kudan V-MMS

  • Easily acquire high-quality point clouds at a low cost.
    -> Offered at up to one-tenth the price.
  • Advanced map integration using relocalization technology refined through our robotics business.
    -> Can be integrated with 3rd party point cloud
  • A simple, user-friendly UI that includes elements of automation.
    -> Can be used without any professional backgrounds.

Examples of Outputs

We provide a wide range of high-quality outputs, not only generating point clouds but also combining them with cutting-edge AI technology.

Colorised 3D point cloud

Semantic segmentation
(3D point cloud)

Georeferenced high-precision image

Example of On-Vehicle Installation

Characteristics

Hardware:
Simple configuration that can be retrofitted to existing vehicles.

Simple UI:
Intuitive operation with no specialized knowledge required.

Noise Filtering:
Advanced noise filtering that integrates object recognition technology.

GCP Correction:
Simple UI for ground control point (GCP) input and map-wide accuracy correction.

Map Handling：
Advanced map integration with 3rd party point clouds is also possible.

Blue: 3rd party
pointcloud

White: Kudan
pointcloud
Red: Difference
 between 2
pointclouds

Semantic Segmentation:
Segmentation of objects within a point cloud using AI image recognition technology.

Use cases

Surveying

Disaster prevention and management

Next-generation applications through 3D Point Clouds x Imagery x AI