Mobile robot autonomous navigation using MEMS gyro north finding method in Global Urban System

Developing an autonomous indoor mobile robot navigation system from scratch is very difficult and it takes a long time. It requires a series of complex tasks such as handling given sensor inputs, calculating all the information needed for autonomous navigation, and controlling actuators required for movement. In this paper, an autonomous navigation system for indoor mobile robots is introduced mainly based on open source provided by the robot operating system. The presented system is capable of autonomously navigating an unstructured indoor environment avoiding collision with static or dynamic objects. To this end, the system consists of three main modules: mapping, localization, and planning. The mapping module builds a global map for an unknown environment by means of a simultaneous localization and mapping algorithm based on laser scanner data. The localization module estimates the mobile robot’s pose within the prebuilt map by way of an adaptive Monte Carlo localization approach. The planning module builds a local cost map for collision avoidance, generates collision-free trajectories to reach a goal pose based on the cost map, and produces control commands to follow the trajectories. The presented system has been tested not only in simulation environments built in the Gazebo simulator but also in real environments utilizing the Jackal mobile robot, to validate its performance for autonomous navigation including collision avoidance.

Simultaneous localization and mapping (SLAM) is an important system for making fully autonomous robot navigation system. A robot must have a good understanding of the environment for navigation. Mobile robot using fixed laser range finder can only detect obstacle on a particular plane level. This may lead to missing of important obstacles that are both above or below the laser scan level. This will cause the map generated to be inaccurate and collision may happen during autonomous navigation. The Microsoft Kinect sensor can replace a laser range finder in a mobile robot as a low cost alternative while providing 3D data. Using depth image data from Kinect, the obstacles between ground surface and 500mm high are projected to horizontal surface, and converted to laser scan data. This will include the entire obstacles that the robot might collide into. Simulations and experiments have been conducted to test out the method in SLAM process in an indoor environment. Both simulation and experimental results show that the proposed method is capable of getting more detailed map compared to the normal 2D map.

For autonomous navigation in workspace, a mobile robot has to be able to know its position in this space in a precise way that means that the robot must be able to self-localize to move and perform successfully the different entrusted tasks. At present, one of the most used systems in open spaces is the GPS navigation system; however, in indoor spaces (factories, buildings, hospitals, warehouses...) GPS signals are not operative because their intensity is too weak. The absence of GPS navigation systems in these environments has stimulated the development of new local positioning systems with their particular problems. Such systems have required in many cases the installation of beacons that operate like satellites (similar to GPS), the use of landmarks or even the use of other auxiliary systems to determine the robot’s position. The problem of mobile robot localization is a part of a more global problem because in autonomous navigation when a robot is exploring an unknown environment, it usually needs to obtain some important information: a map of the environment and the robot’s location in the map. Since mapping and localization are related to each other, these two problems are usually considered as a single problem called simultaneous localization and mapping (SLAM). The problem of Simultaneous Localization and Map Building is a significant open problem in mobile robotics which is difficult because of the following paradox: to localize itself the robot needs the map of the environment, and, for building a map the robot location must be known precisely. Mobile robots use different kinds of sensors to determine their position: for instance it is very common the use of odometric or inertial sensors, however it is remarkable to consider that in wheel slippage, sensor drifts a noise causing error accumulation, thus leading to erroneous estimates. Another kind of external sensors used in robotics in order to solve localization are for instance CCD cameras, infrared sensor, ultra sonic sensor, mechanical wave and laser. Other sensors recently applied are the instruments sensible to the magnetic field known as the electronic compass (Navarro & Benet, 2009). Mobile robotics are interested on those able to measure the Earths magnetic field and express it through an electrical signal. One type of electronic compass is based on magneto-resistive transducers, whose electrical resistance varies with the changes on the applied magnetic field. This type of sensors presents sensitivities below 0.1 milligauss, with response times below 1 sec, allowing its reliable use in vehicles moving at high speeds (Caruso, 2000). In SLAM some applications with electronic compass have been developed working simultaneously with other sensors such as artificial vision (Kim et al., 2006) and ultrasonic sensors (Kim et al., 2007).

The key to effective autonomous mobile robot navigation is accurate self-localization. Without self-localization or with inaccurate self-localization, any non-holonomic autonomous mobile robot is blind in a navigation environment. The KCLBOT [1] is a nonholonomic two wheeled mobile robot that is built around the specifications for ‘Micromouse Robot’ and the ‘RoboCup’ competition. These specifications contribute to the mobile robot’s form factor and size. The mobile robot holds a complex electronic system to support on-line path planning, self-localization, and even simultaneous localization and mapping (SLAM), which is made possible by an onboard sensor array. The mobile robot is loaded with eight Robot-Electronics SRF05 [2] ultrasonic rangers, and its drive system is supported by Nubotics WC-132 [3] WheelCommander Motion Controller and two WW-01 [4] WheelWatcher Encoders. The motors for robot are modified continuous rotation servo motors, which are required for the WheelCommander Motion Controller. The rotation of the mobile robot is measured by Robot-Electronics CMPS03 [5] Compass Module. These two modules make the double compass configuration, which supports the self-localization theory presented in this paper. Each individual module provides the bearing of the mobile robot relative to the magnetic field of the earth. The central processing for the mobile robot is managed by a Savage Innovations OOPic-R microcontroller. The OOPic-R has advanced communication modules to enable data exchange between the sensors and motion controller. Communication is managed via a serial bus and an 2 I C bus. The electronics are all mounted on two aluminium bases, which make the square structure of the mobile robot. To support the hardware requirements of the novel localisation methodology, the cutting edge technology of a 200 MHz 32-bit ARM 9 processor on a GHI ChipworkX module [6] is employed. The software architecture is based on the Microsoft .NET Micro Framework 4.1 using C# and the Windows Presentation Foundation (WPF). The combination of hardware electronics and drive mechanics, makes the KCLBOT, as represented in Fig. 1, a suitable platform for autonomous self-localization. Many different systems have been considered for self-location, from using visual odometry [7] to using a GPS method. While all of these have benefits and detriments, the solution

This work compared the performance of two Simultaneous Localisation and Mapping (SLAM) algorithms, Hector SLAM and Gmapping, for self-navigation of a mobile robot in a small, slippery surface and controlled environment. The experiment utilised the Bveeta Mini mobile robot within a tiled corridor area. The primary objective was to evaluate and compare the accuracy of these algorithms in self-navigating the robot using acquired robot positions in 2D coordinates. The experiment involved manual mapping of the environment using both Gmapping and Hector SLAM, followed by autonomous navigation tasks with each algorithm. Performance was assessed by comparing the absolute error, absolute relative error, and percentage error between the robot's position obtained from the manual map and its position during autonomous navigation provided by the SLAM algorithms. It was found that the Hector SLAM achieved higher accuracy in all navigation paths than Gmapping. Gmapping suffered from significant errors, particularly in the robot's initial position, likely due to its reliance on odometry data, which was highly susceptible to errors from the slippery surface in the experimental area. In conclusion, both algorithms can be integrated with other advanced SLAM techniques to improve the accuracy of the generated map and robot position.

Although the actual visual simultaneous localization and mapping (SLAM) algorithms provide highly accurate tracking and mapping, most algorithms are too heavy to run live on embedded devices. In addition, the maps they produce are often unsuitable for path planning. To mitigate these issues, we propose a completely closed-loop online dense RGB-D SLAM algorithm targeting autonomous indoor mobile robot navigation tasks. The proposed algorithm runs live on an NVIDIA Jetson board embedded on a two-wheel differential-drive robot. It exhibits lightweight three-dimensional mapping, room-scale consistency, accurate pose tracking, and robustness to moving objects. Further, we introduce a navigation strategy based on the proposed algorithm. Experimental results demonstrate the robustness of the proposed SLAM algorithm, its computational efficiency, and its benefits for on-the-fly navigation while mapping.

A precise localization system and a map that properly represents the environment are fundamental for several robotic applications. Traditional LiDAR SLAM algorithms are particularly susceptible to underestimating the distance covered by real robots in environments with few geometric features. Common industrial confined spaces, such as ducts and galleries, have long and homogeneous structures, which are difficult to map. In this paper, we propose a novel approach, the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">EKF-LOAM</i> , which fuses wheel odometry and IMU (Inertial Measurement Unit) data into the LeGO-LOAM algorithm using an Extended Kalman Filter. For that, the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">EKF-LOAM</i> uses a simple and lightweight adaptive covariance matrix based on the number of detected geometric features. Simulated and real-world experiments with the EspeleoRobô, a service robot designed to inspect confined places, show that the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">EKF-LOAM</i> method reduces the underestimating problem, with improvements greater than 50% when compared to the original LeGO-LOAM algorithm. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper is motivated by the challenges of autonomous navigation for mobile ground robots within confined and unstructured environments. Here, we propose a data fusion framework that uses common sensors (such as LiDARs, wheel odometry, and inertial devices) to improve the simultaneous localization and mapping (SLAM) capabilities of a robot without GPS and compass. This approach does not need artificial landmarks nor ideal light and, in scenarios with few geometric features, increases the performance of LiDAR SLAM techniques based on edge and planar features. We also provide a robust controller for the autonomous navigation of the robot during the mapping of a tunnel. Experiments carried out in simulation and real-world confined places show the effectiveness of our approach. In future work, we shall incorporate other sensors, such as cameras, to improve the SLAM process.

Mapping is one of the mobile robot's most basic applications. A mobile robot's sensors, such as a laser sensor, sonar, and camera, are used to create the map. Most mapping techniques use simultaneous localization and Mapping (SLAM). SLAM allows for creating a map and the localization of the robot's position on it. This research compares the trajectories of a mobile robot created by several ROS-based SLAM systems. And also, GMapping and Karto SLAM are two well-known SLAM algorithms employed. The mobile robot is equipped with 2D lidar and monocular camera. The mapping is done at two distinct locations, in labs of varying sizes with varying numbers of static and dynamic objects. Three test runs are conducted for GMapping to examine the effects of various variables on mapping quality, including particle filter, mapping delay, and robot speed. The results show a significant difference in operation completion time and mapping accuracy as a result of the parameter changing over the three test runs. Due to the improved accuracy of the parameter used in the second test run of GMapping and Karto SLAM, the accuracy of the maps is the basis for this improvement. On the other hand, the second test run with robot particle filter 30, mapping delay 1, and speed 0.13m/s is thought to be the best3\2 13Q/0.

Recently, considerable attention has been focused on the investigation of the use of wireless sensor networks (WSN) to drive mobile robot for efficient exploration in unknown environments. The process of navigation depends heavily on estimating the position of the features provided by sensor within the entire environment. This paper proposes a novel navigation algorithm based on RF wireless sensor networks to simultaneous localization and mapping (SLAM) for an indoor autonomous mobile robot. Triangulation localization method is employed to make the robot locates itself and knows its pose. A simple yet efficient two-step particle filter is applied for map building. Simulation and experimental results show that good localization can be achieved using the proposed method.

Simultaneous Localization and Mapping (SLAM) is one of the most challenging research areas within computer and machine vision for automated scene commentary and explanation. The SLAM technique has been a developing research area in the robotics context during recent years. By utilizing the SLAM method robot can estimate the different positions of the robot at the distinct points of time which can indicate the trajectory of robot as well as generate a map of the environment. SLAM has unique traits which are estimating the location of robot and building a map in the various types of environment. SLAM is effective in different types of environment such as indoor, outdoor district, Air, Underwater, Underground and Space. Several approaches have been investigated to use SLAM technique in distinct environments. The purpose of this paper is to provide an accurate perceptive review of case history of SLAM relied on laser/ultrasonic sensors and camera as perception input data. In addition, we mainly focus on three paradigms of SLAM problem with all its pros and cons. In the future, use intelligent methods and some new idea will be used on visual SLAM to estimate the motion intelligent underwater robot and building a feature map of marine environment.

Promising SLAM Methods for Automated Guided Vehicles and Autonomous Mobile Robots

In this work, we tested Simultaneous localization and mapping (SLAM) about mobile robots in indoor environment, where all experiments were conducted based on the Robot Operating System (ROS). The urban search and rescue(USAR) environment was build in the ROS simulation tool Gazebo, and our car was used to test hector SLAM in Gazebo. The rplidar A1 single-line lidar was used for 2D laser scan matching data acquisition in the practical experiments and the indoor map was built by using the open source algorithms gmapping, karto SLAM, hector SLAM software package for indoor SLAM, which can get the indoor grid maps in ROS graphical tool RVIZ. The experimental results of the three open source algorithms show that the mobile robot for simultaneous localization and mapping (SLAM) is feasible, and high-precision grid maps can be constructed.

Simultaneous Localization and Mapping (SLAM) technology has important implications for the autonomous navigation of intelligent mobile robots. In the past few years, many excellent visual SLAM systems were born, and most of them can do a good job in a static environment. However, in dynamic scenes, unreliable feature points in the scene will lead to the decline of system positioning accuracy and even cause system failure. Traditional methods often use the removal of dynamic points to solve dynamic scene problems, but in some environments, the reduction of feature points will also affect the positioning accuracy. Therefore, based on the ORB-SLAM2 visual SLAM system, this paper proposes a semantic point and line SLAM system for an indoor dynamic environment. The improved SLAM system has good performance in an indoor dynamic environment. Finally, we evaluate our algorithm on the TUM RGB-D dynamic dataset. The results show that the absolute Trajectory Accuracy of SPL-SLAM is significantly improved compared with the original ORB-SLAM2.

This paper proposed a new algorithm for optical flow-based monocular vision (MV)/ inertial navigation system (INS) integrated navigation. In this mode, a downward-looking camera is used to get the image sequences, which is used to estimate the velocity of the mobile robot by using optical flow algorithm. INS is employed for the yaw variation. In order to evaluate the performance of the proposed method, a real indoor test has done. The result shows that the proposed method has good performance for velocity estimation. It can be applied to the autonomous navigation of mobile robots when the Global Positioning System (GPS) and code wheel is unavailable.

Simultaneous localization and mapping (SLAM) becomes popular in the autonomous robot research community. SLAM requests a mobile robot, placed at an unknown location in an unknown environment, to incrementally build a consistent map of this environment while simultaneously determining its location within this map. The odometer data is helpful but the noisy error accumulates. Assistant with measurements from the other sensors, SLAM improves the accuracy by filtering. The extended Kalman filter is the most used method to feedback the error. In this thesis, a monocular vision based SLAM for a wheeled robot is proposed. A single web camera is the only measurement input to correct the odometer data. Extract features and match them based on the scale invariant feature transform (SIFT). Absence of the feature depth information is the key issue for the monocular vision in SLAM. The inverse depth parametrization method solves the problem by present the position in inverse coordinate with some reasonable assumptions. Convergence of the feature position is more easily achieved by the EKF due to the linearity. Combining the odometer data with the inverse depth method, this system provides an accurate map by tests of the indoor image sequence.