SOFT2: Stereo Visual Odometry for Road Vehicles Based on a Point-to-Epipolar-Line Metric

Cvišić, Igor; Marković, Ivan

doi:10.1109/tro.2022.3188121

Cited by 29 publications

(20 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The perception methods such as object classification, object tracking, and vehicle localisations make sense of raw data that are brought after the pre-processing step by sensors and V2X communication. AD and ICC can use cameras to estimate velocity using visual odometry, yaw, roll, and pitch rates, detect road lines [ 149 , 150 ], road curve angle and road bank angle, intersections, railroad, and pedestrian crossings, road surface type and conditions [ 118 ], road lanes and boundaries [ 151 , 152 ], road signs including warnings and speed limits [ 153 ], and horizontal marking detection and recognition [ 154 ], road damage, potholes, and distress detection [ 155 ], location [ 156 ], velocity and displacement of the vehicle [ 157 ] and other vehicles on the road even using their taillights [ 158 ]. ICC systems can use previously described advanced sensors to acquire horizon prediction, and longitudinal and lateral road surface slopes, which, together with in-vehicle IMU and GNSS/INS, will enable a more accurate decomposition of linear and gravitation-caused acceleration.…”

Section: Common Controller Layout For Automated Vehiclesmentioning

confidence: 99%

Review of Integrated Chassis Control Techniques for Automated Ground Vehicles

Skrickij,

Kojis,

Šabanovič

et al. 2024

Sensors

View full text Add to dashboard Cite

Integrated chassis control systems represent a significant advancement in the dynamics of ground vehicles, aimed at enhancing overall performance, comfort, handling, and stability. As vehicles transition from internal combustion to electric platforms, integrated chassis control systems have evolved to meet the demands of electrification and automation. This paper analyses the overall control structure of automated vehicles with integrated chassis control systems. Integration of longitudinal, lateral, and vertical systems presents complexities due to the overlapping control regions of various subsystems. The presented methodology includes a comprehensive examination of state-of-the-art technologies, focusing on algorithms to manage control actions and prevent interference between subsystems. The results underscore the importance of control allocation to exploit the additional degrees of freedom offered by over-actuated systems. This paper systematically overviews the various control methods applied in integrated chassis control and path tracking. This includes a detailed examination of perception and decision-making, parameter estimation techniques, reference generation strategies, and the hierarchy of controllers, encompassing high-level, middle-level, and low-level control components. By offering this systematic overview, this paper aims to facilitate a deeper understanding of the diverse control methods employed in automated driving with integrated chassis control, providing insights into their applications, strengths, and limitations.

show abstract

Section: Common Controller Layout For Automated Vehiclesmentioning

confidence: 99%

Review of Integrated Chassis Control Techniques for Automated Ground Vehicles

Skrickij,

Kojis,

Šabanovič

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…Other researchers [26,27,28] used deep neural networks to eliminate the scale ambiguity of monocular cameras and extract high-level semantic features to enhance the system robustness and accuracy. The classic variants of stereo SLAM include ORBSLAM2 [29], ORBSLAM3 [30], PL-SLAM [31], and SOFT2 [32]. An event camera [33] was used to address the problems of high dynamics and low light, and the depth estimation of multiple viewpoints was merged in a probabilistic manner to build a semidense point cloud map.…”

Section: Related Workmentioning

confidence: 99%

Robust Localization Algorithm for Indoor Robots Based on the Branch-and-Bound Strategy

Zhang,

Wang,

Luo

et al. 2024

J. Engg. Res. & Sci.

View full text Add to dashboard Cite

Robust and accurate localization is crucial for mobile robot navigation in complex indoor environments. This paper introduces a robust and integrated robot localization algorithm designed for such environments. The proposed algorithm, named Branch-and-Bound for Robust Localization (BB-RL), introduces an innovative approach that seamlessly integrates global localization, position tracking, and resolution of the kidnapped robot problem into a single, comprehensive framework. The process of global localization in BB-RL involves a two-stage matching approach, moving from a broad to a more detailed analysis. This method combines a branch-and-bound algorithm with an iterative nearest point algorithm, allowing for an accurate initial estimation of the robot's position. For ongoing position tracking, BB-RL uses a local map-based scan matching technique. To address inaccuracies that accumulate over time in the local maps, the algorithm creates a pose graph which helps in loop-closure optimization. Additionally, to make loop-closure detection less computationally intensive, the branchand-bound algorithm is used to speed up finding loop constraints. A key feature of BB-RL is its Finite State Machine (FSM)-based relocalization judgment method, which is designed to quickly identify and resolve the kidnapped robot problem. This enhances the reliability of the localization process. BB-RL's performance was thoroughly tested in real-world situations using commercially available logistics robots. These tests showed that BB-RL is fast, accurate, and robust, making it a practical solution for indoor robot localization.

show abstract

“…In the front-end, two common methods are the feature-based method [ 21 , 23 , 24 , 25 , 26 ] and the direct method (including the semi-direct method) [ 27 , 28 , 29 ]. ORB-SLAM2 [ 24 ] is a classic feature-point-based Visual SLAM system that estimates camera motion based on feature point extraction, matching, and optimization of the reprojection error.…”

Section: Related Workmentioning

confidence: 99%

“…During the depth recovery of road feature points using stereo disparity, compared to non-road feature points, there is greater depth uncertainty. Consequently, the spatial accuracy of ground feature points is lower, making them unsuitable for direct use in vehicle pose estimation and road model fitting [ 18 , 21 , 22 ].…”

Section: Introductionmentioning

confidence: 99%

RC-SLAM: Road Constrained Stereo Visual SLAM System Based on Graph Optimization

Zhu,

An,

Wang

et al. 2024

Sensors

View full text Add to dashboard Cite

Intelligent vehicles are constrained by road, resulting in a disparity between the assumed six degrees of freedom (DoF) motion within the Visual Simultaneous Localization and Mapping (SLAM) system and the approximate planar motion of vehicles in local areas, inevitably causing additional pose estimation errors. To address this problem, a stereo Visual SLAM system with road constraints based on graph optimization is proposed, called RC-SLAM. Addressing the challenge of representing roads parametrically, a novel method is proposed to approximate local roads as discrete planes and extract parameters of local road planes (LRPs) using homography. Unlike conventional methods, constraints between the vehicle and LRPs are established, effectively mitigating errors arising from assumed six Degrees of Freedom (DoF) motions in the system. Furthermore, to avoid the impact of depth uncertainty in road features, epipolar constraints are employed to estimate rotation by minimizing the distance between road feature points and epipolar lines, robust rotation estimation is achieved despite depth uncertainties. Notably, a distinctive nonlinear optimization model based on graph optimization is presented, jointly optimizing the poses of vehicle trajectories, LPRs, and map points. The experiments on two datasets demonstrate that the proposed system achieved more accurate estimations of vehicle trajectories by introducing constraints between the vehicle and LRPs. The experiments on a real-world dataset further validate the effectiveness of the proposed system.

show abstract

SOFT2: Stereo Visual Odometry for Road Vehicles Based on a Point-to-Epipolar-Line Metric

Cited by 29 publications

References 51 publications

Review of Integrated Chassis Control Techniques for Automated Ground Vehicles

Review of Integrated Chassis Control Techniques for Automated Ground Vehicles

Robust Localization Algorithm for Indoor Robots Based on the Branch-and-Bound Strategy

RC-SLAM: Road Constrained Stereo Visual SLAM System Based on Graph Optimization

Contact Info

Product

Resources

About