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3D map creation system, "MapIV Engine"

The MapIV Engine is a 3D map creation system which gathers SLAM technology originally developed by Map IV, Inc. Creating 3D maps based on SLAM had problems in conditions such as partial accuracy, processing time, the consistency of the entire map, the up-to-dateness of the map, and noise caused by moving objects. Our system solves the above problems by utilizing the latest research results and fusing different sensors including LiDAR, cameras, GNSS, IMU, and odometry. Utilizing the MapIV Engine allows the user to create high-precision three-dimensional dot groups from available data.    

GNSS/IMU location estimation "Eagleye"

Eagleye is a GNSS/IMU location estimation system developed in cooperation with Meguro Laboratory, Meijo University. Location estimation systems employing GNSS/IMU used to require expensive sensors, and the introduction of such systems was very costly. Thanks to the utilization of GNSS Doppler and algorithms for purposes such as the compensation of errors in sensor data, Eagleye achieves location estimation accuracy comparable to that achieved by expensive systems, even when Eagleye utilizes inexpensive sensors. Furthermore, Eagleye is publicly disclosed as open-source software and is a system that can be used by any user. On the basis of feedback from users, we will further improve the performance of Eagleye.
Source code:  
https://github.com/MapIV/eagleye

Open-source location estimation system "Iris"

Iris is an open-source location estimation system which utilizes 3D maps and images captured by monocular cameras. As for location estimation based on 3D maps, in many cases, methods based on scan matching utilizing LiDAR data are applied. However, the drawbacks are that LiDAR is expensive, and that it is costly to integrate LiDAR with small-sized systems. Accordingly, we developed a technology which uses cameras that are relatively less expensive than LiDAR, for 3D location estimation. The technology converts image feature points obtained from camera images into a three-dimensional dot group which will be matched with an existing 3D map. In the process of the matching, scale estimation, which is an issue specific to VisualSLAM, is performed, enabling the acquisition of metric location information.  
Source code: https://github.com/MapIV/iris

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Chubu Head Office, SMRJ

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