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

TL;DR
This paper presents an affordable SLAM method for construction machines that combines IMU and differential GPS to create environmental maps, improving decision-making without relying on costly sensors.
Contribution
The study introduces a low-cost SLAM approach using an unscented Kalman filter with IMU and GPS, suitable for dynamic construction sites without high-definition maps.
Findings
Average distance error below 2 meters
Mapping error less than 1.3% in harsh environments
Effective in dynamic construction site conditions
Abstract
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 dynamically 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 gird map for the construction site so that the machine can have the environmental information and be optimized…
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Taxonomy
TopicsIndoor and Outdoor Localization Technologies · Robotics and Sensor-Based Localization · Modular Robots and Swarm Intelligence
