Proceedings ArticleDOI
Stereo odometry based on careful feature selection and tracking
Igor Cvišić,Ivan Petrović +1 more
- pp 1-6
TLDR
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.read more
Citations
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Journal ArticleDOI
Simultaneous Localization and Mapping: A Survey of Current Trends in Autonomous Driving
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.
Book
Computer Vision for Autonomous Vehicles: Problems, Datasets and State-of-the-Art
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.
Proceedings ArticleDOI
LIMO: Lidar-Monocular Visual Odometry
TL;DR: A depth extraction algorithm from LIDAR measurements for camera feature tracks and estimating motion by robustified keyframe based Bundle Adjustment is proposed, and semantic labeling is used for outlier rejection and weighting of vegetation landmarks.
Posted Content
Computer Vision for Autonomous Vehicles: Problems, Datasets and State of the Art
TL;DR: In this paper, the authors provide a survey on the state-of-the-art datasets and techniques for autonomous driving, including recognition, reconstruction, motion estimation, tracking, scene understanding, and end-to-end learning.
Journal ArticleDOI
SOFT‐SLAM: Computationally efficient stereo visual simultaneous localization and mapping for autonomous unmanned aerial vehicles
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.
References
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Proceedings ArticleDOI
Are we ready for autonomous driving? The KITTI vision benchmark suite
TL;DR: The autonomous driving platform is used to develop novel challenging benchmarks for the tasks of stereo, optical flow, visual odometry/SLAM and 3D object detection, revealing that methods ranking high on established datasets such as Middlebury perform below average when being moved outside the laboratory to the real world.
Proceedings ArticleDOI
Good features to track
Jianbo Shi,Tomasi +1 more
TL;DR: A feature selection criterion that is optimal by construction because it is based on how the tracker works, and a feature monitoring method that can detect occlusions, disocclusions, and features that do not correspond to points in the world are proposed.
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An efficient solution to the five-point relative pose problem
TL;DR: The algorithm is used in a robust hypothesize-and-test framework to estimate structure and motion in real-time with low delay and is the first algorithm well-suited for numerical implementation that also corresponds to the inherent complexity of the problem.
Proceedings ArticleDOI
SVO: Fast semi-direct monocular visual odometry
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