Evaluation of Sift and Surf for Vision Based Localization

2006

In this paper we present a prototype system for image based localization in urban environments. Given a database of views of city street scenes tagged by GPS locations, the system computes the GPS location of a novel query view. We first use a wide-baseline matching technique based on SIFT features to select the closest views in the database. Often due to a large change of viewpoint and presence of repetitive structures, a large percentage of matches (> 50%) are not correct correspondences. The subsequent motion estimation between the query view and the reference view, is then handled by a novel and efficient robust estimation technique capable of dealing with large percentage of outliers. This stage is also accompanied by a model selection step among the fundamental matrix and the homography. Once the motion between the closest reference views is estimated, the location of the query view is then obtained by triangulation of translation directions. Approximate solutions for cases when triangulation cannot be obtained reliably are also described. The presented system is tested on the dataset used in ICCV 2005 Computer Vision Contest and is shown to have higher accuracy than previous reported results.

2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2009

In the recent past, the recognition and localization of objects based on local point features has become a widely accepted and utilized method. Among the most popular features are currently the SIFT features, the more recent SURF features, and region-based features such as the MSER. For time-critical application of object recognition and localization systems operating on such features, the SIFT features are too slow (500-600 ms for images of size 640×480 on a 3 GHz CPU). The faster SURF achieve a computation time of 150-240 ms, which is still too slow for active tracking of objects or visual servoing applications. In this paper, we present a combination of the Harris corner detector and the SIFT descriptor, which computes features with a high repeatability and very good matching properties within approx. 20 ms. While just computing the SIFT descriptors for computed Harris interest points would lead to an approach that is not scaleinvariant, we will show how scale-invariance can be achieved without a time-consuming scale space analysis. Furthermore, we will present results of successful application of the proposed features within our system for recognition and localization of textured objects. An extensive experimental evaluation proves the practical applicability of our approach.

2013

Post-processing after matching technique such as RANSAC is useful for reducing outliners. However, such methods may not be able to increase the number of correctly matched pairs that is important in some application such as image stitching. In this work, a post-processing technique for increasing the number of correct matched points between two images with on-plane rotation is proposed. The proposed method makes use of the dominant rotational degree between two corresponding images to increase the number of matched points after feature extraction process using state-of-the-art methods such as Scale Invariant Feature Transform (SIFT) or Speeded-Up Robust Feature (SURF). The proposed method can generally increase the number of matched points around 10% to 20%. Furthermore, it can also correct the false matching which is caused by similar appearance of features.

2011 International Conference on Digital Image Computing: Techniques and Applications, 2011

Scene classification in indoor and outdoor environments is a fundamental problem to the vision and robotics community. Scene classification benefits from image features which are invariant to image transformations such as rotation, illumination, scale, viewpoint, noise etc. Selecting suitable features that exhibit such invariances plays a key part in classification performance. This paper summarizes the performance of two robust feature detection algorithms namely Scale Invariant Feature Transform (SIFT) and Speeded up Robust Features (SURF) on several classification datasets. In this paper, we have proposed three shorter SIFT descriptors. Results show that the proposed 64D and 96D SIFT descriptors perform as well as traditional 128D SIFT descriptors for image matching at a significantly reduced computational cost. SURF has also been observed to give good classification results on different datasets.

International Journal of Technology Diffusion, 2020

An autonomous robot is now an internationally discussed topic to ease the life of humans. Localization and movement are two rudimentary necessities of the autonomous robots before accomplishing any job. So, many researchers have proposed methods of localization using external tools like network connectivity, global positioning system (GPS), etc. However, if these tools are lost, either the movement will be paused, or the robot will be derailed from the actual mission. In these circumstances, the authors propose an approach to localize an autonomous robot in a specific area using the given set of images without external help. The image database has been prepared and kept in the internal memory of robot so that image matching can be done quickly. The localization method has been accomplished using three algorithms: (1) SURF, (2) ICP-BP, and (3) EMD. In the evaluation, SURF has been found better than ICP-BP and EMD in terms of accuracy and elapsed time. The authors believe that the proposed method will add value to other methods using some external tools even when those tools are unavailable.

2020

In recent years there has been a great deal of research and study in the field of visual odometry, which has led to the development of practical processes such as visual based measurement in robotics and automotive technology. Direct methods, feature-based methods and hybrid methods are three common approaches in solving visual odometry problems and given the general belief that feature-based approach speeds are higher, this approach has been welcomed in recent years. Therefore, an attempt has been made in the present study to calculate the transformation matrix of two-dimensional sequential image sets using invariant features that can estimate the changes in camera rotation and translation. In the algorithm, two-steps of identifying keypoints and removing outliers are performed using five different local feature detection algorithms (SURF, SIFT, FAST, STAR, ORB) and RANdom SAmple Consensus algorithm (RANSAC), respectively. In addition, the impact of each of them, their intrinsic pa...

2002

We have previously developed a mobile robot system which uses scale invariant visual landmarks to localize and simultaneously build a 3D map of the environment In this paper, we look at global localization, also known as the kidnapped robot problem, where the robot localizes itself globally, without any prior location estimate. This is achieved by matching distinctive landmarks in the current frame to a database map. A Hough Transform approach and a RANSAC approach for global localization are compared, showing that RANSAC is much more efficient. Moreover, robust global localization can be achieved by matching a small sub-map of the local region built from multiple frames. 0-7803-7398-7/02/$17.00 ©2002 IEEE

2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2007

Vision-based robot localization in outdoor environments is difficult because of changing illumination conditions. Another problem is the rough and cluttered environment which makes it hard to use visual features that are not rotation invariant. A popular method that is rotation invariant and relatively robust to changing illumination is the Scale Invariant Feature Transform (SIFT). However, due to the computationally intensive feature extraction and image matching, localization using SIFT is slow. On the other hand, techniques which use global image features are in general less robust and exact than SIFT, but are often much faster due to fast image matching. In this paper, we present a hybrid localization approach that switches between local and global image features. For most images, the hybrid approach uses fast global features. Only in difficult situations, e.g. containing strong illumination changes, the hybrid approach switches to local features. To decide which features to use for an image, we analyze the particle cloud of the particle filter that we use for position estimation. Experiments on outdoor images taken under varying illumination conditions show that the position estimates of the hybrid approach are about as exact as the estimates of SIFT alone. However, the average localization time using the hybrid approach is more than 3.5 times faster than using SIFT.

International Journal of Computer Applications, 2013

Object Recognition has become one of the most attractive areas of research for most of the scientists over the past few decades. Object recognition has extensive applications in numerous areas of interest. In this paper, the importance of object recognition in different applications has been highlighted. The very famous and impressive technique by David Lowe which is Scale Invariant Feature Transform (SIFT) has been implemented for object recognition and an attempt has been done to compare the results obtained from it with the another very important technique called Speeded-Up Robust Feature Transform (SURF) to conclude with certain interesting results.