Abstract
Simultaneous localization and mapping (SLAM) is a widely used technology in autonomous mobile robots, where sensors such as Lidar or cameras are typically used. Sensor fusion using multiple sensors has been employed to compensate for the shortcomings of each sensor in SLAM. However, the sensor cost cannot be ignored when considering its practical usage. Therefore, this study aims at realizing a high-precision SLAM using a sensor switching system, combining multiple low-cost sensors. The sensor switching system consists of a low-cost Lidar SLAM and a monocular localization. Since a low-cost Lidar has a short laser range, degeneracy often occurs due to the fact that they cannot capture features while building maps. The proposed system uses localization data from monocular localization to ensure precision in regions where degeneracy occurs. The proposed system was evaluated through the simulation assuming the museum environment where the degeneracy occurred. The accuracy of the robot trajectory and the built map proved the effectiveness of the proposed system.
Highlights
Autonomous mobile robots are used in various environments and applications, such as transport robots in factories and service robots in facilities
This study proposes a low cost yet highly accurate Lidar simultaneous localization and mapping (SLAM) under following two conditions: (1) Limited to extremely low-cost sensor configuration of short-range 2D Lidar and monocular camera, (2) Assume an indoor space where monocular SLAM is basically well performed but short-range Lidar SLAM degenerates in some areas
In this study, we proposed a short-range Lidar SLAM that utilizes localization data from the monocular localization as a supplement, with the goal of realizing a high-accuracy SLAM with low-cost sensors in environments where degeneracy occurs
Summary
Autonomous mobile robots are used in various environments and applications, such as transport robots in factories and service robots in facilities. These robots can solve the labor shortage issue caused by the declining birth rate and aging population, save labor, and improve efficiency by automating tasks. Due to the COVID19 pandemic, the demand for autonomous mobile robots that can replace human labor is expected to increase. A more accurate movement of the robots and a reduction in the installation costs are expected. A high-accuracy simultaneous localization and mapping (SLAM) system [1] is required for stable and accurate autonomous movement in practical applications.
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