Where Am I? SLAM for Mobile Machines on a Smart Working Site

Yusheng Xiang, Dianzhao Li, Tianqing Su, Quan Zhou, Christine Brach, Samuel S. Mao, Marcus Geimer

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


The current optimization approaches of construction machinery are mainly based on internal sensors. However, the decision of a reasonable strategy is not only determined by its intrinsic signals, but also very strongly by environmental information, especially the terrain. Due to the dynamic changing of the construction site and the consequent absence of a high definition map, the Simultaneous Localization and Mapping (SLAM) offering the terrain information for construction machines is still challenging. Current SLAM technologies proposed for mobile machines are strongly dependent on costly or computationally expensive sensors, such as RTK GPS and cameras, so that commercial use is rare. In this study, we proposed an affordable SLAM method to create a multi-layer grid map for the construction site so that the machine can have the environmental information and be optimized accordingly. Concretely, after the machine passes by the grid, we can obtain the local information and record it. Combining with positioning technology, we then create a map of the interesting places of the construction site. As a result of our research gathered from Gazebo, we showed that a suitable layout is the combination of one IMU and two differential GPS antennas using the unscented Kalman filter, which keeps the average distance error lower than 2m and the mapping error lower than 1.3% in the harsh environment. As an outlook, our SLAM technology provides the cornerstone to activate many efficiency improvement approaches.

Original languageBritish English
Pages (from-to)529-552
Number of pages24
Issue number2
StatePublished - Jun 2022


  • localization of construction machines
  • ROS
  • SLAM
  • smart working site
  • unscented Kalman filter


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