A Review on Sensor Network Issues and Robotics <s> Localization for Robots <s> The problem of establishing reliable and accurate correspondence between a stored 3-D model and a 2-D image of it is important in many computer vision tasks, including model-based object recognition, autonomous navigation, pose estimation, airborne surveillance, and reconnaissance. This paper presents an approach to solving this problem in the context of autonomous navigation of a mobile robot in an outdoor urban, man-made environment. The robot's environment is assumed consist of polyhedral buildings. The 3-D descriptions of the lines constituting the buildings' rooftops is assumed to be given as the world model. The robot's position and pose are estimated by establishing correspondence between the straight line features extracted from the images acquired by the robot and the model features. The correspondence problem is formulated as a two-stage constrained search problem. Geometric visibility constraints are used to reduce the search space of possible model-image feature correspondences. Techniques for effectively deriving and capturing these visibility constraints from the given world model are presented. The position estimation technique presented is shown to be robust and accurate even in the presence of errors in the feature detection, incomplete model description, and occlusions. Experimental results of testing this approach using a model of an airport scene are presented. <s> BIB001 </s> A Review on Sensor Network Issues and Robotics <s> Localization for Robots <s> To navigate reliably in indoor environments, a mobile robot must know where it is. This includes both the ability of globally localizing the robot from scratch, as well as tracking the robot's position once its location is known. Vision has long been advertised as providing a solution to these problems, but we still lack efficient solutions in unmodified environments. Many existing approaches require modification of the environment to function properly, and those that work within unmodified environments seldomly address the problem of global localization. In this paper we present a novel, vision-based localization method based on the CONDENSATION algorithm, a Bayesian filtering method that uses a sampling-based density representation. We show how the CONDENSATION algorithm can be rued in a novel way to track the position of the camera platform rather than tracking an object in the scene. In addition, it can also be used to globally localize the camera platform, given a visual map of the environment. Based on these two observations, we present a vision-based robot localization method that provides a solution to a difficult and open problem in the mobile robotics community. As evidence for the viability of our approach, we show both global localization and tracking results in the context of a state of the art robotics application. <s> BIB002 </s> A Review on Sensor Network Issues and Robotics <s> Localization for Robots <s> Localization is the process of determining the robot's location within its environment. More precisely, it is a procedure which takes as input a geometric map, a current estimate of the robot's pose, and sensor readings, and produces as output an improved estimate of the robot's current pose (position and orientation). We describe a combinatorially precise algorithm which performs mobile robot localization using a geometric model of the world and a point-and-shoot ranging device. We also describe a rasterized version of this algorithm which we have implemented on a real mobile robot equipped with a laser rangefinder we designed. Both versions of the algorithm allow for uncertainty in the data returned by the range sensor. We also present experimental results for the rasterized algorithm, obtained using our mobile robots at Cornell. <s> BIB003 </s> A Review on Sensor Network Issues and Robotics <s> Localization for Robots <s> Mobile robot localization is the problem of determining a robot’s pose from sensor data. This article presents a family of probabilistic localization algorithms known as Monte Carlo Localization (MCL). MCL algorithms represent a robot’s belief by a set of weighted hypotheses (samples), which approximate the posterior under a common Bayesian formulation of the localization problem. Building on the basic MCL algorithm, this article develops a more robust algorithm called MixtureMCL, which integrates two complimentary ways of generating samples in the estimation. To apply this algorithm to mobile robots equipped with range finders, a kernel density tree is learned that permits fast sampling. Systematic empirical results illustrate the robustness and computational efficiency of the approach.  2001 Published by Elsevier Science B.V. <s> BIB004 </s> A Review on Sensor Network Issues and Robotics <s> Localization for Robots <s> Mobile robots must be able to learn, use and maintain models of their environments. Two fundamental and interrelated problems in mobile robotics are mapping and localization. We present here a new type of image-based map formed by panoramic models enriched with depth and 3D planarity information. We take an advantage of the scene geometry implicitly contained in the model to localize a mobile robot that is moving in the same environment. The novelty of this model compared to the existing image-based maps is that the motion of the robot is not restricted to a predefined path or locations close to the original images. Experimental results demonstrate this approach. <s> BIB005 </s> A Review on Sensor Network Issues and Robotics <s> Localization for Robots <s> Mobile robots need an internal representation of their environment to do useful things. Usually such a representation is some sort of geometric model. For our robot, which is equipped with a panoramic vision system, we choose an appearance model in which the sensoric data (in our case the panoramic images) have to be modeled as a function of the robot position. Because images are very high-dimensional vectors, a feature extraction is needed before the modeling step. Very often a linear dimension reduction is used where the projection matrix is obtained from a Principal Component Analysis (PCA). PCA is optimal for the reconstruction of the data, but not necessarily the best linear projection for the localization task. We derived a method which extracts linear features optimal with respect to a risk measure reflecting the localization performance. We tested the method on a real navigation problem and compared it with an approach where PCA features were used. <s> BIB006 </s> A Review on Sensor Network Issues and Robotics <s> Localization for Robots <s> Locomotion of a biped robot in a scenario with obstacles requires a high degree of coordination between perception and walking. This article presents key ideas of a vision-based strategy for guidance of walking robots in structured scenarios. Computer vision techniques are employed for reactive adaptation of step sequences allowing a robot to step over or upon or walk around obstacles. A highly accurate visual feedback is achieved by a combination of line-based scene analysis and real-time feature tracking. The proposed vision-based approach was evaluated by experiments with a real humanoid robot. I. INTRODUCTION <s> BIB007 </s> A Review on Sensor Network Issues and Robotics <s> Localization for Robots <s> This paper presents a new real-time localization system for a mobile robot. We show that autonomous navigation is possible in outdoor situation with the use of a single camera and natural landmarks. To do that, we use a three step approach. In a learning step, the robot is manually guided on a path and a video sequence is recorded with a front looking camera. Then a structure from motion algorithm is used to build a 3D map from this learning sequence. Finally in the navigation step, the robot uses this map to compute its localization in real-time and it follows the learning path or a slightly different path if desired. The vision algorithms used for map building and localization are first detailed. Then a large part of the paper is dedicated to the experimental evaluation of the accuracy and robustness of our algorithms based on experimental data collected during two years in various environments. <s> BIB008 </s> A Review on Sensor Network Issues and Robotics <s> Localization for Robots <s> For humanoid robots to fully realize their biped potential in a three-dimensional world and step over, around or onto obstacles such as stairs, appropriate and efficient approaches to execution, planning and perception are required. To this end, we have accelerated a robust model-based three-dimensional tracking system by programmable graphics hardware to operate online at frame-rate during locomotion of a humanoid robot. The tracker recovers the full 6 degree-of- freedom pose of viewable objects relative to the robot. Leveraging the computational resources of the GPU for perception has enabled us to increase our tracker's robustness to the significant camera displacement and camera shake typically encountered during humanoid navigation. We have combined our approach with a footstep planner and a controller capable of adaptively adjusting the height of swing leg trajectories. The resulting integrated perception-planning-action system has allowed an HRP-2 humanoid robot to successfully and rapidly localize, approach and climb stairs, as well as to avoid obstacles during walking. <s> BIB009 </s> A Review on Sensor Network Issues and Robotics <s> Localization for Robots <s> Humanoid robots are expected to work in the human environment due to their similarity to the human shape, and they are required to achieve localization and navigation autonomously. In this paper, indoor navigation is realized based on a view-based approach using a camera mounted on the head. In the case of a humanoid robot, image blur and swing due to walking is a crucial issue for image matching during localization. The quantitative effect of walking on the image is firstly investigated by utilizing a motion capture system. Then a method to generate a stable view sequence is proposed based on the detection of optical flows. The navigation function with the proposed method was implemented on a humanoid robot HRP-2, and the effectiveness was confirmed by indoor walking experiments. <s> BIB010
In mobile robotics localization is a key component BIB008 . The process to determine the robots position within the environment is called localization or we can say that it is a process that takes a map as an input and estimates the current pose of the robot, a set of sensor readings, and then outputs the robot's current pose as a new estimate BIB003 . There are many technologies available for robot localization including GPS, active/passive beacons, odometer (dead reckoning), and sonar. For robot localization and map count an algorithm was presented using data from a range based sonar sensor  . For localization the robots position is determined by the algorithm by correlating a local map with a global map. Actually no prior knowledge of the environment is assumed; it uses sensor data to construct the global map dynamically. The algorithm estimates robots location by computing positions called feasible poses where the expected view of robot matches approximately the observed range sensor data. The algorithm then selects the best fit from the feasible poses. It requires robots orientation information to make sure that the algorithm identifies the feasible poses. For location information Vassilis also used dead reckoning as a secondary source; when combined with range sensor based localization algorithm it can provide a close real time location estimate. A Monte Carlo localization algorithm was introduced using (MHL) was used for mobile robot position estimation BIB004 . They used the Monte Carlo type methods and then combined the advantages of their previous work in which grid based Markov localization with efficiency and accuracy of Kalman filter based techniques was used. MCL method is able to deal with ambiguities and thus can globally localize the robot. As compared to their previous grid based method MCL method has significantly reduced memory requirements while at the same time incorporating sensor measurements at a considerably higher frequency. Based on condensation algorithm the Monte Carlo localization method was proposed in BIB002 . It localizes the robot globally using a scalar brightness measurement when given a visual map of the ceiling. Sensor information of low feature is used by these probabilistic methods specifically in 2D plane and needs the robot to move around for probabilities to gradually converge toward a pack. The pose of the robots was also computed by some researchers based on the appearance. Panoramic image-based model for robot localization was used by Cobzas and Zhang BIB005 ; with the depth and 3D planarity information the panoramic model was constructed, while the matching is based on planar patches. For probabilistic appearance based robot localization BIB006 used panoramic images. For extracting the 15-dimensional feature vectors for Markov localization PCA is applied to hundreds of training images. In urban environments the problem of mobile robot localization was addressed by Talluri and Aggarwal BIB001 by using feature correspondence between images taken by camera on robot and a CAD or similar model of its environment. For localization of car in urban environments,  used an inertial measurement unit and a sensor suite consists of four GPS antennas. Humanoid robots are getting popular as research tools as they offer new viewpoint compared to wheeled vehicle. A lot of work has been done so far on the localization for humanoid robots. In order to estimate the location of the robot BIB010 applied a vision based approach and then compared the current image to previously recorded reference images. In the local environment of the humanoid BIB007 detects objects with given colors and shapes and then determines its pose relative to these objects. With respect to a close object BIB009 localizes the robot to track the 6D pose of a manually initialized object relative to camera by applying a model based approach.