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Author

Igor Cvišić

Bio: Igor Cvišić is an academic researcher from University of Zagreb. The author has contributed to research in topics: Odometry & Visual odometry. The author has an hindex of 4, co-authored 13 publications receiving 224 citations.

Papers
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Proceedings ArticleDOI
01 Jan 2015
TL;DR: A novel algorithm for fast and robust stereo visual odometry based on feature selection and tracking (SOFT), which employs an IMU for outlier rejection and Kalman filter for rotation refinement and which outperforms all other validated methods.
Abstract: In this paper we present a novel algorithm for fast and robust stereo visual odometry based on feature selection and tracking (SOFT). The reduction of drift is based on careful selection of a subset of stable features and their tracking through the frames. Rotation and translation between two consecutive poses are estimated separately. The five point method is used for rotation estimation, whereas the three point method is used for estimating translation. Experimental results show that the proposed algorithm has an average pose error of 1.03% with processing speed above 10 Hz. According to publicly available KITTI leaderboard, SOFT outperforms all other validated methods. We also present a modified IMU-aided version of the algorithm, fast and suitable for embedded systems. This algorithm employs an IMU for outlier rejection and Kalman filter for rotation refinement. Experiments show that the IMU based system runs at 20 Hz on an ODROID U3 ARM-based embedded computer without any hardware acceleration. Integration of all components is described and experimental results are presented.

118 citations

Journal ArticleDOI
TL;DR: This paper proposes a stereo vision SLAM yielding very accurate localization and a dense map of the environment developed with the aim to compete in the European Robotics Challenges (EuRoC) targeting airborne inspection of industrial facilities with small‐scale UAVs.
Abstract: Autonomous navigation of unmanned aerial vehicles (UAVs) in GPS-denied environments is a challenging problem, especially for small-scale UAVs characterized by a small payload and limited battery autonomy. A possible solution to the aforementioned problem is vision-based simultaneous localization and mapping (SLAM), since cameras, due to their dimensions, low weight, availability, and large information bandwidth, circumvent all the constraints of UAVs. In this paper, we propose a stereo vision SLAM yielding very accurate localization and a dense map of the environment developed with the aim to compete in the European Robotics Challenges (EuRoC) targeting airborne inspection of industrial facilities with small-scale UAVs. The proposed approach consists of a novel stereo odometry algorithm relying on feature tracking (SOFT), which currently ranks first among all stereo methods on the KITTI dataset. Relying on SOFT for pose estimation, we build a feature-based pose graph SLAM solution, which we dub SOFT-SLAM. SOFT-SLAM has a completely separate odometry and mapping threads supporting large loop-closing and global consistency. It also achieves a constant-time execution rate of 20 Hz with deterministic results using only two threads of an onboard computer used in the challenge. The UAV running our SLAM algorithm obtained the highest localization score in the EuRoC Challenge 3, Stage IIa–Benchmarking, Task 2. Furthermore, we also present an exhaustive evaluation of SOFT-SLAM on two popular public datasets, and we compare it to other state-of-the-art approaches, namely ORB-SLAM2 and LSD-SLAM. The results show that SOFT-SLAM obtains better localization accuracy on the majority of datasets sequences, while also having a lower runtime.

108 citations

Journal ArticleDOI
TL;DR: This work uses a multisensor setup consisting of a radar and a stereo camera mounted on top of a vehicle to model the sensors uncertainty in polar coordinates on Lie Groups and perform the objects state filtering on Lie groups on the product of two special Euclidean groups.

58 citations

Journal ArticleDOI
TL;DR: The control of a robotic system for automation in manufacturing logistics based on the Open Platform for Innovations in Logistics (OPIL) is presented and a novel omnidirectional automated guided vehicle (AGV) suited for transporting the Euro-pallets with the payload of up to 400 kg is developed.

11 citations

Proceedings ArticleDOI
01 Aug 2021
TL;DR: In this article, a new approach for one shot calibration of the KITTI dataset multiple camera setup was proposed, which yields better calibration parameters, both in the sense of lower calibration reprojection errors and lower visual odometry error.
Abstract: Over the last decade, one of the most relevant public datasets for evaluating odometry accuracy is the KITTI dataset. Beside the quality and rich sensor setup, its success is also due to the online evaluation tool, which enables researchers to bench-mark and compare algorithms. The results are evaluated on the test subset solely, without any knowledge about the ground truth, yielding unbiased, overfit free and therefore relevant validation for robot localization based on cameras, 3D laser or combination of both. However, as any sensor setup, it requires prior calibration and rectified stereo images are provided, introducing dependence on the default calibration parameters. Given that, a natural question arises if a better set of calibration parameters can be found that would yield higher odometry accuracy. In this paper, we propose a new approach for one shot calibration of the KITTI dataset multiple camera setup. The approach yields better calibration parameters, both in the sense of lower calibration reprojection errors and lower visual odometry error. We conducted experiments where we show for three different odometry algorithms, namely SOFT2, ORB-SLAM2 and VISO2, that odometry accuracy is significantly improved with the pro-posed calibration parameters. Moreover, our odometry, SOFT2, in conjunction with the proposed calibration method achieved the highest accuracy on the official KITTI scoreboard with 0.53% translational and 0.0009 deg/m rotational error, outperforming even 3D laser-based methods.

8 citations


Cited by
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Journal ArticleDOI
04 Sep 2017
TL;DR: This paper presents the limits of classical approaches for autonomous driving and discusses the criteria that are essential for this kind of application, as well as reviewing the methods where the identified challenges are tackled.
Abstract: In this paper, we propose a survey of the Simultaneous Localization And Mapping (SLAM) field when considering the recent evolution of autonomous driving. The growing interest regarding self-driving cars has given new directions to localization and mapping techniques. In this survey, we give an overview of the different branches of SLAM before going into the details of specific trends that are of interest when considered with autonomous applications in mind. We first present the limits of classical approaches for autonomous driving and discuss the criteria that are essential for this kind of application. We then review the methods where the identified challenges are tackled. We mostly focus on approaches building and reusing long-term maps in various conditions (weather, season, etc.). We also go through the emerging domain of multivehicle SLAM and its link with self-driving cars. We survey the different paradigms of that field (centralized and distributed) and the existing solutions. Finally, we conclude by giving an overview of the various large-scale experiments that have been carried out until now and discuss the remaining challenges and future orientations.

597 citations

Book
03 Jul 2020
TL;DR: This survey includes both the historically most relevant literature as well as the current state of the art on several specific topics, including recognition, reconstruction, motion estimation, tracking, scene understanding, and end-to-end learning for autonomous driving.
Abstract: Recent years have witnessed enormous progress in AI-related fields such as computer vision, machine learning, and autonomous vehicles. As with any rapidly growing field, it becomes increasingly difficult to stay up-to-date or enter the field as a beginner. While several survey papers on particular sub-problems have appeared, no comprehensive survey on problems, datasets, and methods in computer vision for autonomous vehicles has been published. This monograph attempts to narrow this gap by providing a survey on the state-of-the-art datasets and techniques. Our survey includes both the historically most relevant literature as well as the current state of the art on several specific topics, including recognition, reconstruction, motion estimation, tracking, scene understanding, and end-to-end learning for autonomous driving. Towards this goal, we analyze the performance of the state of the art on several challenging benchmarking datasets, including KITTI, MOT, and Cityscapes. Besides, we discuss open problems and current research challenges. To ease accessibility and accommodate missing references, we also provide a website that allows navigating topics as well as methods and provides additional information.

579 citations

Journal ArticleDOI
TL;DR: This paper presents this extended version of RTAB‐Map and its use in comparing, both quantitatively and qualitatively, a large selection of popular real‐world datasets, outlining strengths, and limitations of visual and lidar SLAM configurations from a practical perspective for autonomous navigation applications.

513 citations

Journal ArticleDOI
TL;DR: The current situation of multi-sensor fusion in the automated driving process is analyzed to provide more efficient and reliable fusion strategies and provide some suggestions for further improvement in the future.
Abstract: With the significant development of practicability in deep learning and the ultra-high-speed information transmission rate of 5G communication technology will overcome the barrier of data transmission on the Internet of Vehicles, automated driving is becoming a pivotal technology affecting the future industry. Sensors are the key to the perception of the outside world in the automated driving system and whose cooperation performance directly determines the safety of automated driving vehicles. In this survey, we mainly discuss the different strategies of multi-sensor fusion in automated driving in recent years. The performance of conventional sensors and the necessity of multi-sensor fusion are analyzed, including radar, LiDAR, camera, ultrasonic, GPS, IMU, and V2X. According to the differences in the latest studies, we divide the fusion strategies into four categories and point out some shortcomings. Sensor fusion is mainly applied for multi-target tracking and environment reconstruction. We discuss the method of establishing a motion model and data association in multi-target tracking. At the end of the paper, we analyzed the deficiencies in the current studies and put forward some suggestions for further improvement in the future. Through this investigation, we hope to analyze the current situation of multi-sensor fusion in the automated driving process and provide more efficient and reliable fusion strategies.

182 citations

Journal ArticleDOI
29 Jul 2020-Sensors
TL;DR: This article provides a comprehensive review of the state-of-the-art methods utilized to improve the performance of AV systems in short-range or local vehicle environments and focuses on recent studies that use deep learning sensor fusion algorithms for perception, localization, and mapping.
Abstract: Autonomous vehicles (AV) are expected to improve, reshape, and revolutionize the future of ground transportation. It is anticipated that ordinary vehicles will one day be replaced with smart vehicles that are able to make decisions and perform driving tasks on their own. In order to achieve this objective, self-driving vehicles are equipped with sensors that are used to sense and perceive both their surroundings and the faraway environment, using further advances in communication technologies, such as 5G. In the meantime, local perception, as with human beings, will continue to be an effective means for controlling the vehicle at short range. In the other hand, extended perception allows for anticipation of distant events and produces smarter behavior to guide the vehicle to its destination while respecting a set of criteria (safety, energy management, traffic optimization, comfort). In spite of the remarkable advancements of sensor technologies in terms of their effectiveness and applicability for AV systems in recent years, sensors can still fail because of noise, ambient conditions, or manufacturing defects, among other factors; hence, it is not advisable to rely on a single sensor for any of the autonomous driving tasks. The practical solution is to incorporate multiple competitive and complementary sensors that work synergistically to overcome their individual shortcomings. This article provides a comprehensive review of the state-of-the-art methods utilized to improve the performance of AV systems in short-range or local vehicle environments. Specifically, it focuses on recent studies that use deep learning sensor fusion algorithms for perception, localization, and mapping. The article concludes by highlighting some of the current trends and possible future research directions.

181 citations