Multi-LiDAR Mapping for Scene Segmentation in Indoor Environments for Mobile Robots (original) (raw)

Indoor Scanning and Mapping using Mobile Robot and RP Lidar

International Journal of Advances in Mechanical & Automobile Engineering, 2016

The environment scanning has been important in a mobile robot studies and applications that may help people to investigate, monitor and study a variety of environments such as an unexplored, hazardous, dynamic, cluttered and others. The main issue regarding this research is the efficiency of the mobile robot system and the accuracy of the front-end sensor. This paper presents a work on the development of a mobile robot for the environment scanning purposes where a laser range finder (LRF) is utilized as its scanning sensor. The development of the mobile robot system will be explained here which consists of the hardware / software, the path following navigation and the integration of the LRF system. Three environments were used as the testbeda corridor, a research lab and a robotic lab. The scanning results are presented in local map and global map which are plotted by using MATLAB software. It is found out that the plotted maps are similar to the real environment, which can be concluded that the developed mobile robot with the LRF is a success and can be used in real applications.

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

Sensors, 2022

This paper attempts to uncover one possible method for the IMR (indoor mobile robot) to perform indoor exploration associated with SLAM (simultaneous localization and mapping) using LiDAR. Specifically, the IMR is required to construct a map when it has landed on an unexplored floor of a building. We had implemented the e-SLAM (exploration-based SLAM) using the coordinate transformation and the navigation prediction techniques to achieve that purpose in the engineering school building which consists of many 100-m2 labs, corridors, elevator waiting space and the lobby. We first derive the LiDAR mesh for the orthogonal walls and filter out the static furniture and dynamic humans in the same space as the IMR. Then, we define the LiDAR pose frame including the translation and rotation from the orthogonal walls. According to the MSC (most significant corner) obtained from the intersection of the orthogonal walls, we calculate the displacement of the IMR. The orientation of the IMR is cal...

Lidar-Based Mobile Mapping System for an Indoor Environment

Slovak Journal of Civil Engineering

The article deals with developing and testing a low-cost measuring system for simultaneous localisation and mapping (SLAM) in an indoor environment. The measuring system consists of three orthogonally-placed 2D lidars, a robotic platform with two wheel speed sensors, and an inertial measuring unit (IMU). The paper describes the data processing model used for both the estimation of the trajectory of SLAM and the creation of a 3D model of the environment based on the estimated trajectory of the SLAM. The main problem of SLAM usage is the accumulation of errors caused by the imperfect transformation of two scans into each other. The data processing developed includes an automatic evaluation and correction of the slope of the lidar. Furthermore, during the calculation of the trajectory, a repeatedly traversed area is identified (loop closure), which enables the optimisation of the trajectory determined. The system was tested in the indoor environment of the Faculty of Civil Engineering ...

Mapping with synthetic 2D LIDAR in 3D urban environment

2013 IEEE/RSJ International Conference on Intelligent Robots and Systems

In this paper, we report a fully automated detailed mapping of a challenging urban environment using single LIDAR. To improve scan matching, extended correlative scan matcher is proposed. Also, a Monte Carlo loop closure detection is implemented to perform place recognition efficiently. Automatic recovery of the pose graph map in the presence of false place recognition is realized through a heuristic based loop closure rejection. This mapping framework is evaluated through experiments on the real world dataset obtained from NUS campus environment.

2D mapping using omni-directional mobile robot equipped with LiDAR

TELKOMNIKA Telecommunication Computing Electronics and Control, 2020

A room map in a robot environment is needed because it can facilitate localization, automatic navigation, and also object searching. In addition, when a room is difficult to reach, maps can provide information that is helpful to humans. In this study, an omni-directional mobile robot equipped with a LiDAR sensor has been developed for 2D mapping a room. The YDLiDAR X4 sensor is used as an indoor scanner. Raspberry Pi 3 B single board computer (SBC) is used to access LiDAR data and then send it to a computer wirelessly for processing into a map. This computer and SBC are integrated in robot operating system (ROS). The movement of the robot can use manual control or automatic navigation to explore the room. The Hector SLAM algorithm determines the position of the robot based on scan matching of the LiDAR data. The LiDAR data will be used to determine the obstacles encountered by the robot. These obstacles will be represented in occupancy grid mapping. The experimental results show that the robot is able to follow the wall using PID control. The robot can move automatically to construct maps of the actual room with an error rate of 4.59%. Keywords: 2D mapping LiDAR Omni-directional mobile robot SLAM This is an open access article under the CC BY-SA license.

Indoor Scanning and Mapping using Mobile Robot and RP Lidar Marni

2016

The environment scanning has been important in a mobile robot studies and applications that may help people to investigate, monitor and study a variety of environments such as an unexplored, hazardous, dynamic, cluttered and others. The main issue regarding this research is the efficiency of the mobile robot system and the accuracy of the front-end sensor. This paper presents a work on the development of a mobile robot for the environment scanning purposes where a laser range finder (LRF) is utilized as its scanning sensor. The development of the mobile robot system will be explained here which consists of the hardware / software, the path following navigation and the integration of the LRF system. Three environments were used as the testbed – a corridor, a research lab and a robotic lab. The scanning results are presented in local map and global map which are plotted by using MATLAB software. It is found out that the plotted maps are similar to the real environment, which can be co...

Tamas, Goron - 2012 - 3D map building with mobile robots

This paper presents a feature-based registration for 3D environments using mobile robots. The developed 3D laser scanner with custom hardware setup is able to scan both indoor and outdoor. For the map registration a nonlinear variant of the Iterative Closest Point (ICP) algorithm was used with initial alignment from the correspondences given by the features of the scenes. The initial alignment was determined using a set of key-points and the features of the keypoints in order to reduce the computational time and to ensure a robust estimation. Considering the increasing interest in 3D navigation for mobile robots, our aim was to use the created maps for both indoor and outdoor navigation purposes. Several maps were built by merging point clouds while our method was tested for a wide range of datasets including urban and office environments.

Autonomous Robot Mapping by Landmark Association

2015

This paper shows how an indoor mobile robot equipped with a laser sensor and an odometer computes its global map by associating landmarks found in the environment. The approach developed is based on the observation that humans and animals detects where they are in the surrounding by comparing their spatial relation to some known or recognized objects in the environments, i.e. landmarks. In this case, landmarks are defined as 2D surfaces detected in the robot’s surroundings. They are recognised if they are detected in two successive views. From a cognitive standpoint, this work is inspired by two assumptions about the world; (a) the world is relatively stable and (2) there is a significant overlap of spatial information between successive views. In the implementation, the global map is first initialised with the robot’s first view, and then updated each time landmarks are found at every two successive views. The difference here is, where most robot mapping work integrates everything ...

Evaluation of Modern Laser Based Indoor SLAM Algorithms

2018 22nd Conference of Open Innovations Association (FRUCT), 2018

One of the key issues that prevents creation of a truly autonomous mobile robot is the simultaneous localization and mapping (SLAM) problem. A solution is supposed to estimate a robot pose and to build a map of an unknown environment simultaneously. Despite existence of different algorithms that try to solve the problem, the universal one has not been proposed yet [1]. A laser rangefinder is a widespread sensor for mobile platforms and it was decided to evaluate actual 2D laser scan based SLAM algorithms on real world indoor environments. The following algorithms were considered: Google Cartographer [2], GMapping [3], tinySLAM [4]. According to their evaluation, Cartographer and GMapping are more accurate than tinySLAM and Cartographer is the most robust of the algorithms.

LOCUS: A Multi-Sensor Lidar-Centric Solution for High-Precision Odometry and 3D Mapping in Real-Time

IEEE Robotics and Automation Letters , 2020

A reliable odometry source is a prerequisite to enable complex autonomy behaviour in next-generation robots operating in extreme environments. In this work, we present a high-precision lidar odometry system to achieve robust and real-time operation under challenging perceptual conditions. LOCUS (Lidar Odometry for Consistent operation in Uncertain Settings), provides an accurate multi-stage scan matching unit equipped with an health-aware sensor integration module for seamless fusion of additional sensing modalities. We evaluate the performance of the proposed system against state-ofthe-art techniques in perceptually challenging environments, and demonstrate top-class localization accuracy along with substantial improvements in robustness to sensor failures. We then demonstrate real-time performance of LOCUS on various types of robotic mobility platforms involved in the autonomous exploration of the Satsop power plant in Elma, WA where the proposed system was a key element of the CoSTAR team’s solution that won first place in the Urban Circuit of the DARPA Subterranean Challenge.