Performance Evaluation of Various 2-D Laser Scanners for Mobile Robot Map Building and Localization

A study has been carried out to investigate the performance of various 2-D laser scanners, which influence the map building quality and localization performance for a mobile robot. Laser scanners are increasingly used in automation and robotic applications. They are widely used as sensing devices for map building and localization in navigation of mobile robot. Laser scanners are commercially available, but there is very little published information on the performance comparison of various laser scanners on the mobile robot map building and localization. Hence, this work studies the performance by comparing four laser scanners which are Hokuyo URG04LX-UG01, Hokuyo UTM30LX, SICK TIM551 and Pepperl Fuchs ODM30M. The results, which are verified by comparison with the reference experimental data, indicated that the angle resolution and sensing range of laser scanner are key factors affecting the map building quality and position estimation for localization. From the experiment, laser scanner with 0.25° angle resolution is optimum enough for building a map of sufficient quality for good localization performance. With 30meter of sensing range, a laser scanner can also result in better localization performance, especially in big environment

Increasing the Efficiency of 6-DoF Visual Localization Using Multi-Modal Sensory Data

Localization is a key requirement for mobile robot autonomy and human-robot interaction. Vision-based localization is accurate and flexible, however, it incurs a high computational burden which limits its application on many resource-constrained platforms. In this paper, we address the problem of performing real-time localization in large-scale 3D point cloud maps of ever-growing size. While most systems using multi-modal information reduce localization time by employing side-channel information in a coarse manner (eg. WiFi for a rough prior position estimate), we propose to inter-weave the map with rich sensory data. This multi-modal approach achieves two key goals simultaneously. First, it enables us to harness additional sensory data to localise against a map covering a vast area in real-time; and secondly, it also allows us to roughly localise devices which are not equipped with a camera. The key to our approach is a localization policy based on a sequential Monte Carlo estimator. The localiser uses this policy to attempt point-matching only in nodes where it is likely to succeed, significantly increasing the efficiency of the localization process. The proposed multi-modal localization system is evaluated extensively in a large museum building. The results show that our multi-modal approach not only increases the localization accuracy but significantly reduces computational time.Comment: Presented at IEEE-RAS International Conference on Humanoid Robots (Humanoids) 201

Simultaneous Localization and Mapping (SLAM) on NAO

Simultaneous Localization and Mapping (SLAM) is a navigation and mapping method used by autonomous robots and moving vehicles. SLAM is mainly concerned with the problem of building a map in an unknown environment and concurrently navigating through the environment using the map. Localization is of utmost importance to allow the robot to keep track of its position with respect to the environment and the common use of odometry proves to be unreliable. SLAM has been proposed as a solution by previous research to provide more accurate localization and mapping on robots. This project involves the implementation of the SLAM algorithm in the humanoid robot NAO by Aldebaran Robotics. The SLAM technique will be implemented using vision from the single camera attached to the robot to map and localize the position of NAO in the environment. The result details the attempt to implement specifically the chosen algorithm, 1-Point RANSAC Inverse Depth EKF Monocular SLAM by Dr Javier Civera on the robot NAO. The algorithm is shown to perform well for smooth motions but on the humanoid NAO, the sudden changes in motion produces undesirable results.This study on SLAM will be useful as this technique can be widely used to allow mobile robots to map and navigate in areas which are deemed unsafe for humans

Three-dimensional localization and mapping for mobile robot in disaster environments

To relieve damages of earthquake disaster, &#34;The Special Project for Earthquake Disaster Mitigation in Urban Areas&#34; have been kicked off in Japan. Our research group is a part of the sub-project &#34;modeling of disaster environment for search and rescue&#34; since 2002. In this project, our group aims to develop a three-dimensional mapping's algorithm that is installed in a mobile robot to search victims in a collapsed building. To realize this mission, it is important to map environment information, and also the mapping requires localization simultaneously. (This is called &#34;SLAM problem&#34;.) In this research, we use three-dimensional map by laser range finder, and we also estimate its location in a global map using correlation technique. In this paper, we introduce our localization and mapping method, and we report a result of preparatory experiment for localization. </p

Topological Global Localization and Mapping with Fingerprint and Uncertainty

Navigation in unknown or partially unknown environments remains one of the biggest challenges in today\'s mobile robotics. Environmental modeling, perception, localization and mapping are all needed for a successful approach. The contribution of this paper resides in the extension of the fingerprint concept (circular list of features around the robot) with uncertainty modeling, in order to improve localization and allow for automatic map building. The uncertainty is defined as the probability of a feature of being present in the environment when the robot perceives it. The whole approach is presented in details and viewed in a topological optic. Experimental results of the perception and localization capabilities with a mobile robot equipped with two 180° laser range finders and an omni-directional camera are reported

Safe Navigation for Indoor Mobile Robots - PartII: Exploration, Self Localization and Map Building

International audienceThis paper is the second part of the author's contribution on the topic of Safe Navigation for Indoor Mobile Robots. It presents a new solution to the exploration, self localization and map building problem taking advantage of the sensor-based navigation framework presented in the paper: Safe Navigation for Indoor Mobile Robots - Part I: A Sensor-based Navigation Framework. The model of the indoor environment is structured as an hybrid representation, both topological and geometrical, which is incrementally built during the exploration task. The topological aspect of the model captures the connectivity and accessibility of the different places in the environment, and the geometrical model holds up an accurate robot localization and map building method. To overpass the problem of drift inherited to the odometry when the robot navigates in large scale environments, a new dead-reckoning method is proposed combining laser readings and feedback control inputs. Embedding the self-localization and map building problem in a sensor-based navigation framework improves both the quality and the robustness of the representation built during the exploration phase and authorizes a further use to achieve safe navigation tasks successfully. Experiments are shown which confirm the interests of the proposed methodology

B2B2: LiDAR 2D Mapping Rover

Autonomous machines are becoming more popular and useful with even self-driving cars being a thing of the present. Most of these machines navigate using cameras and LiDAR which does not detect glass, therefore the machines give misleading results when objects and obstacles are transparent to the wavelengths of the light used. This is problematic in modern building floor plans with glass walls. A solution is to build a ROS system that fuses ultrasonic sensors with LiDAR sensors in order for a robot to navigate in a building that has glass walls. Using both sensors, the final product is a robot that creates a 2D map using Simultaneous Localization and Mapping (SLAM) as well as other pertinent Robotics Operating Systems (ROS) packages. This map enables any mobile robot to pathplan from point A to B on the now created 2D floor plan that incorporates glass and non-glass obstacles. This saves time and energy when compared to a robot that moves from point A to B that has to continuously change paths in the presence of obstacles