Hybrid Monocular SLAM Using Double Window Optimization

Luo, Hu; Pape, Christian; Reithmeier, Eduard

doi:10.1109/lra.2021.3070298

Cited by 10 publications

(6 citation statements)

References 25 publications

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“…[ 227 ] leveraged direct tracking adopted from LSD-SLAM [ 216 ] for inter-keyframe pose tracking and feature-based tracking for incremental motion estimation, which also served as a form of motion prior to keyframe refinement. A similar notion was adopted and improved upon in [ 228 ]. Conversely, in [ 229 ] the direct module from DSO [ 222 ] was used for real-time camera tracking, and the feature-based module from ORB-SLAM [ 157 ] was used for globally consistent pose refinement.…”

Section: Sensors and Sensor-based Odometry Methodsmentioning

confidence: 99%

Sensors and Sensor Fusion Methodologies for Indoor Odometry: A Review

et al. 2022

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Although Global Navigation Satellite Systems (GNSSs) generally provide adequate accuracy for outdoor localization, this is not the case for indoor environments, due to signal obstruction. Therefore, a self-contained localization scheme is beneficial under such circumstances. Modern sensors and algorithms endow moving robots with the capability to perceive their environment, and enable the deployment of novel localization schemes, such as odometry, or Simultaneous Localization and Mapping (SLAM). The former focuses on incremental localization, while the latter stores an interpretable map of the environment concurrently. In this context, this paper conducts a comprehensive review of sensor modalities, including Inertial Measurement Units (IMUs), Light Detection and Ranging (LiDAR), radio detection and ranging (radar), and cameras, as well as applications of polymers in these sensors, for indoor odometry. Furthermore, analysis and discussion of the algorithms and the fusion frameworks for pose estimation and odometry with these sensors are performed. Therefore, this paper straightens the pathway of indoor odometry from principle to application. Finally, some future prospects are discussed.

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Section: Sensors and Sensor-based Odometry Methodsmentioning

confidence: 99%

Sensors and Sensor Fusion Methodologies for Indoor Odometry: A Review

et al. 2022

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“…This architecture may need some extra computing resources because the system must maintain a feature-based method and a direct method simultaneously. Meanwhile, some works optimize the reprojection error to get a coarse camera pose firstly, and then apply a direct image alignment algorithm to refine the result (Kim et al, 2019;Krombach et al, 2016;Luo et al, 2021), which can be implemented in a unified form (Younes et al, 2019). SVO (Forster et al, 2016) is an extremely efficient hybrid system that performs direct image alignment using features detected for initial pose estimation before refining it with a (Engel J et al, 2016).…”

Section: Vslam/vo Systemmentioning

confidence: 99%

Hybrid sparse monocular visual odometry with online photometric calibration

Zhuang

Wang

2022

The International Journal of Robotics Research

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Most monocular visual Simultaneous Localization and Mapping (vSLAM) and visual odometry (VO) algorithms focus on either feature-based methods or direct methods. Hybrid (semi-direct) approach is less studied although it is equally important. In this paper, a hybrid sparse visual odometry (HSO) algorithm with online photometric calibration is proposed for monocular vision. HSO introduces two novel measures, that is, direct image alignment with adaptive mode selection and image photometric description using ratio factors, to enhance the robustness against dramatic image intensity changes and motion blur. Moreover, HSO is able to establish pose constraints between keyframes far apart in time and space by using KLT tracking enhanced with a local-global brightness consistency. The convergence speed of candidate map points is adopted as the basis for keyframe selection, which strengthens the coordination between the front end and the back end. Photometric calibration is elegantly integrated into the VO system working in tandem: (1) Photometric interference from the camera, such as vignetting and changes in exposure time, is accurately calibrated and compensated in HSO, thereby improving the accuracy and robustness of VO. (2) On the other hand, VO provides pre-calculated data for the photometric calibration algorithm, which reduces resource consumption and improves the estimation accuracy of photometric parameters. Extensive experiments are performed on various public datasets to evaluate the proposed HSO against the state-of-the-art monocular vSLAM/VO and online photometric calibration methods. The results show that the proposed HSO achieves superior performance on VO and photometric calibration in terms of accuracy, robustness, and efficiency, being comparable with the state-of-the-art VO/vSLAM systems. We open source HSO for the benefit of the community.

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“…Hybrid approaches [12], [41], [42] use both photometric and geometric errors, while the photometric model provides accurate pose estimation over short-term tracking without data association, the geometric model gives robustness for a large baseline. A representative work by Forster et al [12] proposed semidirect VO, where the short-term tracking is solved by minimizing the photometric error, while windowed BA minimizes a reprojection error built from previously established matching pairs.…”

Section: B Photometric Approachesmentioning

confidence: 99%

Photometric Visual-Inertial Navigation With Uncertainty-Aware Ensembles

2022

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In this article, we propose a visual-inertial navigation system that directly minimizes a photometric error without an explicit data-association. We focus on the photometric error parametrized by pose and structure parameters that is highly nonconvex due to the nonlinearity of image intensity. The key idea is to introduce an optimal intensity gradient that accounts for a projective uncertainty of a pixel. Ensembles sampled from the state uncertainty contribute to the proposed gradient and yield a correct update direction even in a bad initialization point. We present two sets of experiments to demonstrate the strengths of our framework. First, a thorough Monte Carlo simulation in a virtual trajectory is designed to reveal robustness to large initial uncertainty. Second, we show that the proposed framework can achieve superior estimation accuracy with efficient computation time over state-of-the-art visual-inertial fusion methods in a real-world UAV flight test, where most scenes are composed of a featureless floor.

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Hybrid Monocular SLAM Using Double Window Optimization

Cited by 10 publications

References 25 publications

Sensors and Sensor Fusion Methodologies for Indoor Odometry: A Review

Sensors and Sensor Fusion Methodologies for Indoor Odometry: A Review

Hybrid sparse monocular visual odometry with online photometric calibration

Photometric Visual-Inertial Navigation With Uncertainty-Aware Ensembles

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