Medical Mobile Robot Localization in Hospital Corridor Environment Using Laser SLAM and Text Features

Abstract

Localization is an important capability for a medical service robot navigating in the hospital environment. Recently, by using laser-based simultaneous localization and mapping (SLAM) and Monte Carlo localization (MCL) techniques, the mobile robot can localize itself in those places with obvious geometric features, like corners, lines, breakpoints, and curves. However, when a robot moves in a long corridor environment inside a building, there are only a few geometric features for reference, especially in symmetrical or similar areas. Consequently, the mobile robot can neither localize itself correctly in the global localization phase without a known initial pose nor recover the correct pose from the kidnapped robot problem. To solve this problem, we propose an approach that uses text features on the doorplate to assist localization. In the mapping stage, we control the mobile robot moves along a fixed path using both text features and laser scans data. In the localization stage, the mobile robot moving according to a strategy to find text features and initializes the particles, then navigates and localizes using an improved MCL method. The experimental results show that our approach achieves over 90% mapping accuracy and 93.3% successful localization rate, which outperforms traditional methods.

Simultaneous Monocular Visual Odometry and Depth Reconstruction with Scale Recovery

“…In 2023, Ge et al [29] proposed a visual-feature-assisted localization methods in long corridor environments.A novel coarse-to-fine paradigm was presented that uses visual features to assist mobile robot localization in long corridors.Sufficient keyframes are obtained through the camera, and a visual camera map was created while the grid map built by the laser-based SLAM method with a low accuacy in corridors, and the mobile robot captures images in a proper perspective according to the moving strategy and matches them with the image map to achieve a coarse localization. The proposed method consists of two main threads: the edge extraction thread ,which main function is to extract ground edges line Sets, and the depth recovery thread, witch mainly completes the depth estimation of the scene.…”

Feature-Based Occupancy Map-Merging for Collaborative SLAM

“…The solution to the SLAM problem is considered to be one of the fundamental requirements to achieve true robot autonomy. The SLAM could be extremely useful during scenarios when the global positioning system (GPS) is inaccessible, e.g., in environments such as indoor [ 2 ], drone [ 3 ], underwater [ 4 ], forest canopy [ 5 ], medical [ 6 ], etc. However, the interdependence nature of localization and mapping makes SLAM a complex and challenging problem in practical applications.…”