Simultaneous localization and map building for mobile robot navigation (original) (raw)

Mapping and Localization of Autonomous Mobile Robots in Simulated Indoor Environments

2024

Autonomously making a map, localizing within it, and planning with it are fundamental problems in mobile robotics. Every autonomous mobile robot system must include a solution to all three problems. These three problems are interconnected, with simultaneous localization and mapping (SLAM) being a well-known issue. However, there is indeed a growing and developing realization in the research field that path planning how a robot goes about mapping and finding an environment (and then operating in the environment such as starting to the destination point) can avoid degenerate conditions and greatly reduce SLAM complexity. In this paper, the implementation of an autonomous mobile robot system for indoor environments using open-source ROS packages and a combination of cartography algorithm and adaptive Monte Carlo localization (AMCL) algorithms has been implemented. The system addresses the challenge of developing three components such as mapping, localization, and path planning systems for indoor autonomous mobile robots. The mapping module creates a global map using the cartography ROS package and SLAM algorithm. The localization module estimates the robot's pose using the AMCL approach. The planning module generates collision-free trajectories and control commands using the moving base ROS package. The experimental results demonstrate the effectiveness of this approach and its valuable contribution to the robotics field. The cartography algorithm mapping algorithm generates accurate and reliable maps, while the localization algorithm successfully determines the robot's position with good performance. Additionally, the path planning algorithm effectively avoids both static and dynamic obstacles, ensuring smooth navigation in the environment.

Off-line localisation of a mobile robot using ultrasonic measurements

Robotica, 2000

In the scope of disabled people assistance for object manipulation and carrying, the paper focuses on the localisation for mobile robot autonomy. In order to respect strong low-cost constraints, the perception system of the mobile robot uses sensors of low metrological quality, ultrasonic ring and odometry. That poses new problems for localisation in particular. Among different localisation techniques, we present only off-line localisation. With poor perception means, it is necessary to introduce a priori knowledge on sensors and environment models. To solve the localisation problem, the ultrasonic image is segmented applying Hough transform, well-adapted to ultrasonic sensor characteristics. The segments are then matched with the room, modelled and assumed to be rectangular. Several positions are found. A first sort, based on a cost function, reduces the possibilities. The remaining ambiguities are removed by a neural network. It plays the part of a classifier detecting the door in the environment. Improvements of the method are proposed to take into account obstacles and non rectangular room. Experimental results show that the localisation operates even with one obstacle.

Robot Localization and Map Building

2010

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Algorithms for Maps Construction and Localization in a Mobile Robot

Studies in Informatics and Control, 2014

In this paper, both SLAM and global localization are used for mapping and a mobile robot simultaneous localization (SLAM). This permits a mobile robot moving in an unknown environment and its implementation in closed environments (e.g. rooms, offices, warehouses, etc.) with autonomous exploration

Simultaneous Localization and Map Building by a Mobile Robot Using Sonar Sensors

2010

This paper presents a methodology to self localization of autonomous mobile robot using two distinct structures of metric maps. Ultrasonic sonar ranger sensors are used to build an occupancy grid, the first structure, and a map based on line segments extracted from the occupancy grid is built. The two maps are used to estimate an correct errors on odometric sensors.

Global Integration of Ultrasonic Sensors Information in Mobile Robot Localization

1999

This paper describes an ultrasonic sensors localization system for autonomous mobile robot navigation in a semi-structured indoor environment. A peripheral ring of 24 ultrasonic sensors is used to obtain the information required for the localization process. The proposed a lgorithm is based upon an extended Kalman filter, which u tilizes matches between ob served g eometric beacons projections and an a priori map o f beacon locations, to correct t he position and orientation o f t he vehicle. The resulting self-localization module has been integrated successfully in a more complicated navigation system. Various experimental results show the effectiveness of the presented algorithm.

Simultaneous localization and mapping in domestic environments

2001

Navigating autonomously in a domestic environment is a problem that has attracted a great deal of interest in mobile robotics. A robotic system that operates in ordinary furnished rooms without the need of an engineered environment has many different applications such as service, cleaning and surveillance tasks or simply entertainment. Robotic systems that use artificial landmarks or pre-stored maps of the environment are available today. However, these systems are not very flexible. The user must in fact supply a map of the environment, which can be interpreted by the system. This thesis deals with the problem of Simultaneous Localization and Mapping (SLAM). The mobile robot builds a map of an unexplored environment while simultaneously using this map to localize itself. The feature based approach used in this thesis utilizes the Extended Kalman Filter (EKF) machinery to estimate the pose of the robot and the location of the features. This approach is referred to as stochastic mapping. Point features in the environment are robustly extracted from sonar data using triangulation techniques. In addition, this thesis explores a method for recovering from the most common mode of failure of the stochastic mapping approach. This method allows the EKF algorithm to continue in a consistent manner after a failure has been detected. Finally, the thesis presents a method for achieving more accurate navigation by using the architectural properties of most domestic environments. This method drastically improves navigation, when the stochastic mapping algorithm can not be used due to poor quality sensor data. All the algorithms presented in this thesis have been tested and verified in real world experiments.

Sensor influence in the performance of simultaneous mobile robot localization and map building

Experimental Robotics VI, 2000

Mobile robot navigation in unknown environments requires the concurrent estimation of the mobile robot localization with respect to a base reference and the construction of a global map of the navigation area. In this paper we present a comparative study of the performance of the localization and map building processes using two distinct sensorial systems: a rotating 2D laser rangefinder, and a trinocular stereo vision system.

Mobile-Robot Map Building from an Advanced Sonar Array and Accurate Odometry

The International Journal of Robotics Research, 1999

This paper describes a mobile robot equipped with a sonar sensor array in a guided feature based map building task in an indoor environment. The landmarks common to indoor environments are planes, corners and edges, and these are located and classified with the sonar sensor array. The map building process makes use of accurate odometry information that is derived from a pair of knife edged unloaded encoder wheels. Discrete sonar observations are incrementally merged into partial planes to produce a realistic representation of environment that is amenable to sonar localisation.

Localization Methods for a Mobile Robot in Urban Environments

IEEE Transactions on Robotics, 2004

This paper addresses the problems of building a functional mobile robot for urban site navigation and modeling with focus on keeping track of the robot location. We have developed a localization system that employs two methods. The first method uses odometry, a compass and tilt sensor, and a global positioning sensor. An extended Kalman filter integrates the sensor data and keeps track of the uncertainty associated with it. The second method is based on camera pose estimation. It is used when the uncertainty from the first method becomes very large. The pose estimation is done by matching linear features in the image with a simple and compact environmental model. We have demonstrated the functionality of the robot and the localization methods with real-world experiments.