Exploration-Based SLAM (e-SLAM) for the Indoor Mobile Robot Using Lidar

Path Planning and Motion Control of Indoor Mobile Robot under Exploration-Based SLAM (e-SLAM)

Indoor mobile robot (IMR) motion control for e-SLAM techniques with limited sensors, i.e., only LiDAR, is proposed in this research. The path was initially generated from simple floor plans constructed by the IMR exploration. The path planning starts from the vertices which can be traveled through, proceeds to the velocity planning on both cornering and linear motion, and reaches the interpolated discrete points joining the vertices. The IMR recognizes its location and environment gradually from the LiDAR data. The study imposes the upper rings of the LiDAR image to perform localization while the lower rings are for obstacle detection. The IMR must travel through a series of featured vertices and perform the path planning further generating an integrated LiDAR image. A considerable challenge is that the LiDAR data are the only source to be compared with the path planned according to the floor map. Certain changes still need to be adapted into, for example, the distance precision with relevance to the floor map and the IMR deviation in order to avoid obstacles on the path. The LiDAR setting and IMR speed regulation account for a critical issue. The study contributed to integrating a step-by-step procedure of implementing path planning and motion control using solely the LiDAR data along with the integration of various pieces of software. The control strategy is thus improved while experimenting with various proportional control gains for position, orientation, and velocity of the LiDAR in the IMR.

A Multi-Layered 3D NDT Scan-Matching Method for Robust Localization in Logistics Warehouse Environments

This paper proposed a multi-layered 3D NDT (normal distribution transform) scan-matching approach for robust localization even in the highly dynamic environment of warehouse logistics. Our approach partitioned a given 3D point-cloud map and the scan measurements into several layers regarding the degree of environmental changes in the height direction and computed the covariance estimates for each layer using 3D NDT scan-matching. Because the covariance determinant is the estimate's uncertainty, we can determine which layers are better to use in the localization in the warehouse. If the layer gets close to the warehouse's floor, the degree of environmental changes, such as the cluttered warehouse layout and position of boxes, would be significantly large, while it has many good features for scan-matching. If the observation at a specific layer is not explained well enough, then the layer for localization can be switched to other layers with lower uncertainties. Thus, the main novelty of this approach is that localization robustness can be improved even in very cluttered and dynamic environments. This study also provides the simulation-based validation using Nvidia's Omniverse Isaac sim and detailed mathematical descriptions for the proposed method. Moreover, the evaluated results of this study can be a good starting point for further mitigating the effects of occlusion in warehouse navigation of mobile robots.

Real-Time 3D Mapping in Isolated Industrial Terrain with Use of Mobile Robotic Vehicle

Simultaneous localization and mapping (SLAM) is a dual process responsible for the ability of a robotic vehicle to build a map of its surroundings and estimate its position on that map. This paper presents the novel concept of creating a 3D map based on the adaptive Monte-Carlo location (AMCL) and the extended Kalman filter (EKF). This approach is intended for inspection or rescue operations in a closed or isolated area where there is a risk to humans. The proposed solution uses particle filters together with data from on-board sensors to estimate the local position of the robot. Its global position is determined through the Rao–Blackwellized technique. The developed system was implemented on a wheeled mobile robot equipped with a sensing system consisting of a laser scanner (LIDAR) and an inertial measurement unit (IMU), and was tested in the real conditions of an underground mine. One of the contributions of this work is to propose a low-complexity and low-cost solution to real-time 3D-map creation. The conducted experimental trials confirmed that the performance of the three-dimensional mapping was characterized by high accuracy and usefulness for recognition and inspection tasks in an unknown industrial environment.