Using the CONDENSATION algorithm for robust, vision-based mobile robot localization

Mobile robot localization and map building are very important for a mobile robot to perform a navigation-based service task to aid the aged or disabled in an indoor environment (office, facilities or at home). In order to enable mobile robot to perform service tasks autonomously, the information of environment in which the mobile robot moves is needed. To obtain the accurate map, many different sensors system and techniques have been developed. Approaches of mapping can roughly be classified according to the kind of sensor data processed and the matching algorithms [S. Thrun et al., 1996, 1998, 2000, 2004; Chong, K.S et al., 1996; C.-C. Wang et al., 2002; G. Dissanayake et al., 2000; B. barshan et al., 1995; Weimin Shen et al., 2005]. In this paper, we proposed a method of map building using interactive GUI for a mobile robot. The reason we proposed this method is that it is difficult for a mobile robot to generate an accurate map although many kinds of sensors are used. The proposed method enables the operator to modify map built by sensors, compared with the real-time video from web camera by using modification tool in interactive GUI. Laser Range Finder (LRF) was used to get the information of the environment in which mobile robot moving. In order to improve self-localization of mobile robot, Extended Kalman Filter (EKF) was introduced. By the results of simulation and experiments, the developed EKF is effective in self-localization of mobile robot. This paper introduces the architecture of the system and gives some experimental results to verify the effectiveness of the developed system. The rest of the paper consists of 5 sections. Section 2 describes the structure of the proposed system. Section 3 presents the developed algorithm of self-localization of mobile robot and gives some simulation and experimental results to verify the effectiveness of the EKF in improving precision of positioning of mobile robot. Section 4 details the developed LRF data processing algorithm. Section 5 introduces the developed interactive GUI. The experimental results are given in Section 6. Section 7 concludes the paper. 13

Owing to the presence of multiple obstacles or changes in the positions of surroundings objects, the localization of a mobile robot using only natural landmarks is difficult in indoor environments. Consequently, in these environments, the use of artificial landmarks to achieve robust localization is necessary. To this end, we propose a global localization method for mobile robots, using reflectors as indistinguishable artificial landmarks, not the manmade signs have a certain pattern. In previous studies, it has been shown that localization is possible using information about the distance between indistinguishable artificial landmarks. We applied this principle in a real-world situation with many obstacles or in a dynamic environment and confirmed that robust, high-accuracy global localization is possible.

This paper presents a novel methods for estimating the position and orientation of a mobile robot using only sonar range data in an indoor environment. The mobile robot senses the environment with two sonar sensors as it moves. It utilizes the specular characteristic to recognize an edge and by referring to the location of the edge, the robot localizes itself rapidly. The data obtained by sensors at time t-1 can be used at time t, and are utilized to get the current position of a mobile robot. The presented algorithms are especially suitable for processing the sonar scan data obtained by ultrasonic sensors with wide beam spread. The validity of the proposed methods have been proved by experiments, and their performances are also investigated.

Global localization problem is one of the classical and important problems in mobile robot. In this paper, we present an approach to solve robot global localization in indoor environments with grid map. It combines Hough Scan Matching (HSM) and grid localization method to get the initial knowledge of robot's pose quickly. For pose tracking, a scan matching technique called Iterative Closest Point (ICP) is used to amend the robot motion model, this can drastically decreases the uncertainty about the robot's pose in prediction step. Then accurate proposal distribution taking into account recent observation is introduced into particle filters to recover the best estimate of robot trajectories, which seriously reduces number of particles for pose tracking. The proposed approach can globally localize mobile robot fast and accurately. Experiment results carried out with robot data in indoor environments demonstrates the effectiveness of the proposed approach.

Mobile robot navigation by image classification using a neural network

Localization is one of many issues in mobile robot study. Localization is an essential ability for mobile robot to determine its location, so that it can plan a movement and go to a desired location. The mutual method for mobile robot localization is using a particle filter. High computation needs in particle filter is one of problem in particle filtering to get accurate location. The paper proposes a low computational mobile robot localization using a particle filter. It uses two methods: local localization using a dead reckoning method and global localization using a landmark-based vision sensor. Simulation results show that the proposed method provides a good estimation on the mobile robot position and orientation.

Generally, the mobile robots are researched and used in many applications. The fundamental method of navigation is localization, which includes aspects such as sensing, mapping, localization, planning and control. In order to we build the navigation system for mobile robot operating in unmodified environments, we must know the mobile robot’s position in reference frame. Typically reference frame is a Cartesian or we can specify the landmark as reference position. The dead reckoning based on odometry is widely to identify and classify landmarks. Dead reckoning systems estimate the position of the mobile robot with respect to an initial point by measuring linear and angular velocities or accelerations. There are also hybrid models, which have sensor systems of both dead reckoning and landmark recognition. Dead reckoning is the estimate of current position based on a previously determined position or estimated speeds from a known past position over time. This can also determine the future position by projecting an ordered course and speed of advance from a known present position. A disadvantage of dead reckoning is that new positions are calculated solely from previous positions. The errors of the process are cumulative, so the error in the position fix grows with time (Hans-Joachim von der Hardt & Didier Wolf Rene Husson, 1996; Hye Ri Park et al., 2009). Odometry is widely used in mobile robots to estimate their position relative to reference frame. This method estimates the position over time with errors accumulation due to the integration of velocity measurements (Cristina Tar in Sauer and Hermann Brugger, 2001; Houshangi, N. and Azizi, F., 2006; Agostino Martinelli, 2002). The navigation system for mobile robot based on odometry has typically problem with inaccuracy. Because of the odometry problems navigation systems for mobile robot application cannot work solely on dead reckoning. This is the reason that the navigation system needs the addition of sensory information. Additional sensors can be defined as information provided to assist the accomplishment of a task by a system. Practically, sensor fusion refers to the integration process where the different sources of sensory information are combined into one representational format. Typically in navigation systems sensory information can be fused from complementary sensors, redundant sensors or even from a single sensor over a period of time. The advantage of sensory fusion provides uncertainty and noise reduction, failure toleration and extended flexibility of sensor ability.

Position estimation based on scan matching is a type of popular approaches for mobile robot localization in indoor office-like environments. Global scan matching and local scan matching are two basic position estimation problems. Global scan matching is subject to global localization for mobile robot and local scan matching is used to deal with tracking mobile robot position over time. Global localization based on scan matching is represented in this paper. A global map is built to represent whole environment feature and a local map is built to represent current range scan. Global map and local map consist of a lot of line segments. Complete line segment that represents a complete environment object is singled out and used to match between local map and global map. Line segments are sorted counterclockwise in two maps. This type of ordinal map improves the searching efficiency of matching and localization algorithm is timesaving. In addition, matching between current range scan and global map is based on relative position relationships of line segments in local and global map. Global localization based on relative position relationship avoids frequent coordinates transformation and lowers computational cost. All these techniques have been implemented on our mobile robot ATRVII equipped with 2D laser range scanner SICK.

Robot navigation and obstacle avoidance are from the most important problems in mobile robots, especially in unknown environments. The autonomous navigation of mobile robots has attracted a number of researchers over the years. A wide variety of approaches and algorithms have been proposed to tackle this complex problem. There are many successful localization methods that can determine the robot's position relative to a map using sonar, laser and camera data; however, most localization methods fail under common environmental conditions. In this paper we present a novel method for navigation of autonomous mobile robots in several indoor environments that optimize our old navigation approach, using fuzzy Kalman filter based on extracted lines and fusing data from laser scanner sensors. In this approach, the sonar data and laser measurement are combined using the fuzzy Kalman filter. Finally, the simulation results show that the proposed algorithm offers advantages over previous methods and has improved performance.

Reliability is a key factor for realizing safety guarantee of fully autonomous robot systems. In this paper, we focus on reliability in mobile robot localization. Monte Carlo localization (MCL) is widely used for mobile robot localization. However, it is still difficult to guarantee its safety because there are no methods determining reliability for MCL estimate. This paper presents a novel localization framework that enables robust localization, reliability estimation, and quick relocalization, simultaneously. The presented method can be implemented using a similar estimation manner to that of MCL. The method can increase localization robustness to environment changes by estimating known and unknown obstacles while performing localization; however, localization failure of course occurs by unanticipated errors. The method also includes a reliability estimation function that enables a robot to know whether localization has failed. Additionally, the method can seamlessly integrate a global localization method via importance sampling. Consequently, quick relocalization from a failure state can be realized while mitigating noisy influence of global localization. We conduct three types of experiments using wheeled mobile robots equipped with a two‐dimensional LiDAR. Results show that reliable MCL that performs robust localization, self‐failure detection, and quick failure recovery can be realized.

Navigation in mobile robotic ambit is a methodology that allows guiding a mobile robot (MR) to accomplish a mission through an environment with obstacles in a good and safe way, and it is one of themost challenging competence required of theMR. The success of this task requires a good coordination of the four main blocks involved in navigation: perception, localization, cognition, and motion control. The perception block allows the MR to acquire knowledge about its environment using sensors. The localization block must determine the position of the MR in the environment. Using the cognition block the robot will select a strategy for achieving its goals. The motion control block contains the kinematic controller, its objective is to follow a trajectory described by its position (Siegwart & Nourbakhsh, 2004). The MR should possess an architecture able to coordinate the on board navigation elements in order to achieve correctly the different objectives specified in the mission with efficiency that can be carried out either in indoor or outdoor environments. In general, global planning methods complemented with local methods are used for indoor missions since the environments are known or partially known; whereas, for outdoormissions local planning methods are more suitable, becoming global planning methods, a complement because of the scant information of the environment. In previous work, we developed a path planning method for wheeled MR navigation using a novel proposal of ant colony optimization named SACOdm (Simple Ant Colony Optimization with distance (d) optimization, and memory (m) capability), considering obstacle avoidance into a dynamic environment (Porta et al., 2009). In order to evaluate the algorithm we used virtual obstacle generation, being indispensable for real world application to have a way of sensing the environment. There are several kind of sensors, broadly speaking they can be classified as passive and active sensors. Passive sensors measure the environmental energy that the sensor receives, in this classification some examples are microphones, tactile sensors, and vision based sensors. Active sensors, emit energy into the environment with the purpose of measuring the environmental reaction. It is common that an MR have several passive and/or active sensors; in our MR, for example, the gear motors use optical quadrature encoders, it uses a high precision GPS for localization, and two video cameras to implement a stereoscopic vision system for object recognition and localization of obstacles for map building and map reconstruction. 3

A robust environment map with 3D spatial natural landmarks that facilitates monocular vision based mobile robot for global localization is built. The highly distinctive multi-dimensional vector descriptors associated with the features extracted through Scale Invariant Feature Transform (SIFT) can be robustly matched despite changes in illumination, scale and viewpoint. The landmarks are 3D restructured with the matching image feature pairs obtained through the KD-Tree based nearest search approach. Novel RANSAC approach based on generic optimization for global localization is presented. Experiments on the robot Pioneer3 with monocular vision in our real indoor environment show that our method is of high precision.

In the current article, we address the problem of constructing radiofrequency identification (RFID)-augmented environments for mobile robots and the issues related to creating user interfaces for efficient remote navigation with a mobile robot in such environments. First, we describe an RFID-based positioning and obstacle identification solution for remotely controlled mobile robots in indoor environments. In the robot system, an architecture specifically developed by the authors for remotely controlled robotic systems was tested in practice. Second, using the developed system, three techniques for displaying information about the position and movements of a remote robot to the user were compared. The experimental visualization techniques displayed the position of the robot on an indoor floor plan augmented with (1) a video view from a camera attached to the robot, (2) display of nearby obstacles (identified using RFID technology) on the floor plan, and (3) both features. In the experiment, test subjects controlled the mobile robot through predetermined routes as quickly as possible avoiding collisions. The results suggest that the developed RFID-based environment and the remote control system can be used for efficient control of mobile robots. The results from the comparison of the visualization techniques showed that the technique without a camera view (2) was the fastest, and the number of steering motions made was smallest using this technique, but it also had the highest need for physical human interventions. The technique with both additional features (3) was subjectively preferred by the users. The similarities and differences between the current results and those found in the literature are discussed.

This article presents a fast self-localization method based on ZigBee wireless sensor network and laser sensor, an obstacle avoidance algorithm based on ultrasonic sensors for a mobile robot. The positioning system and positioning theory of ZigBee which can obtain a rough global localization of the mobile robot are introduced. To realize accurate local positioning, a laser sensor is used to extract the features from environment, then the environmental features and global reference map can be matched. From the matched environmental features, the position and orientation of the mobile robot can be obtained. To enable the mobile robot to avoid obstacle in real-time, a heuristic fuzzy neural network is developed by using heuristic fuzzy rules and the Kohonen clustering network. The experiment results show the effectiveness of the proposed method.

This paper proposes an efficient dynamic localization algorithm in the active beacon sensor space for the precise localization of a mobile robot. The design and implementation of the localization system is based on the use of active beacon systems, each of which is composed of an RFID receiver and an ultra-sonic transmitter. The RFID receiver obtains the synchronization signal from the mobile robot and the ultra-sonic transmitter sends out a short-term signal to be used for measuring the distance from the beacon to the mobile robot. The position of a mobile robot in three dimensional space can basically be calculated using the distance information from three beacons which have their own absolute position information. There exists a localization error caused by the long time required to process the data from the beacons. To overcome this problem, a new method of allocating the beacons in the absolute position sensor space, which is referred to as the dynamic localization algorithm, is proposed in this paper to improve the localization accuracy. That is, an efficient and precise algorithm to estimate the position and orientation of the mobile robot as quickly as possible has been newly developed. The effectiveness of the proposed algorithm is demonstrated and verified through experiments.