On combining visual SLAM and dense scene flow to increase the robustness of localization and mapping in dynamic environments

Alcantarilla, Pablo F.; Yebes, J. Javier; Almazán, Javier; Bergasa, Luis M.

doi:10.1109/icra.2012.6224690

Cited by 137 publications

(99 citation statements)

References 23 publications

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“…Here, the feature temporal flow (Bellavia et al, 2015) is a promising approach. The detection of moving objects may be another task for future works, for example based on the dense scene flow (Alcantarilla et al, 2012). Finally, VO results should directly be coupled with IMU and other measurements, for example using the well-known Kalman filter.…”

Section: Discussionmentioning

confidence: 99%

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Enhancement Strategies for Frame-to-Frame Uas Stereo Visual Odometry

Kersten¹,

Rodehorst²

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Autonomous navigation of indoor unmanned aircraft systems (UAS) requires accurate pose estimations usually obtained from indirect measurements. Navigation based on inertial measurement units (IMU) is known to be affected by high drift rates. The incorporation of cameras provides complementary information due to the different underlying measurement principle. The scale ambiguity problem for monocular cameras is avoided when a light-weight stereo camera setup is used. However, also frame-to-frame stereo visual odometry (VO) approaches are known to accumulate pose estimation errors over time. Several valuable real-time capable techniques for outlier detection and drift reduction in frame-to-frame VO, for example robust relative orientation estimation using random sample consensus (RANSAC) and bundle adjustment, are available. This study addresses the problem of choosing appropriate VO components. We propose a frame-to-frame stereo VO method based on carefully selected components and parameters. This method is evaluated regarding the impact and value of different outlier detection and drift-reduction strategies, for example keyframe selection and sparse bundle adjustment (SBA), using reference benchmark data as well as own real stereo data. The experimental results demonstrate that our VO method is able to estimate quite accurate trajectories. Feature bucketing and keyframe selection are simple but effective strategies which further improve the VO results. Furthermore, introducing the stereo baseline constraint in pose graph optimization (PGO) leads to significant improvements.

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Section: Discussionmentioning

confidence: 99%

“…To reduce uncertainties, for example in case of small motions, we use a simple keyframe selection method similar to (Alcantarilla et al, 2012). The current frame t+1 is a new keyframe, when the accumulated translation or rotation with respect to the last keyframe exceeds a defined threshold.…”

Section: Selection Of Enhancement Strategiesmentioning

confidence: 99%

Enhancement Strategies for Frame-to-Frame Uas Stereo Visual Odometry

Kersten¹,

Rodehorst²

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…Methods relying on inter-frame point correspondences typically use detectors/descriptors such as SIFT [5], SURF [15], FAST [11], Harris [13,21], and even custom designed detectors [9]. Optical flow methods such as KLT [23] and dense scene flow [2] are also typically used. Strategies such as the bucketing technique [28] further help distribute the features more uniformly on the image space to improve the conditioning of the optimization problem [15].…”

Section: Related Literaturementioning

confidence: 99%

Visual Odometry by Multi-frame Feature Integration

Badino

Yamamoto

Kanade

2013

2013 IEEE International Conference on Computer Vision Workshops

109

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This paper presents a novel stereo-based visual odometry approach that provides state-of-the-art results in real time, both indoors and outdoors. Our proposed method follows the procedure of computing optical flow and stereo disparity to minimize the re-projection error of tracked feature points. However, instead of following the traditional approach of performing this task using only consecutive frames, we propose a novel and computationally inexpensive technique that uses the whole history of the tracked feature points to compute the motion of the camera. In our technique, which we call multi-frame feature integration, the features measured and tracked over all past frames are integrated into a single, improved estimate. An augmented feature set, composed of the improved estimates, is added to the optimization algorithm, improving the accuracy of the computed motion and reducing ego-motion drift. Experimental results show that the proposed approach reduces pose error by up to 65% with a negligible additional computational cost of 3.8%. Furthermore, our algorithm outperforms all other known methods on the KITTI Vision Benchmark data set.

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“…Transitions between these situations have been ticked accordingly in the frame when they take place. An example of the operation of our scaling approach is provided in the supplementary video file 1 .…”

Section: Methodsmentioning

confidence: 99%

“…In [2] Badino and Kanade propose head-wearable stereo system to estimate structure and motion. Alcantarilla et al [1] propose a wearable stereo SLAM system which is able to detect moving objects of the scene.…”

Section: Introductionmentioning

confidence: 99%

Scaled monocular SLAM for walking people

Gutiérrez-Gómez

Guerrero

2013

Proceedings of the 2013 International Symposium on Wearable Computers

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In this paper we present a full-scaled real-time monocular SLAM using only a wearable camera. Assuming that the person is walking, the perception of the head oscillatory motion in the initial visual odometry estimate allows for the computation of a dynamic scale factor for static windows of N camera poses. Improving on this method we introduce a consistency test to detect non-walking situations and propose a sliding window approach to reduce the delay in the update of the scaled trajectory. We evaluate our approach experimentally on a unscaled visual odometry estimate obtained with a wearable camera along a path of 886 m. The results show a significant improvement respect to the initial unscaled estimate with a mean relative error of 0.91% over the total trajectory length.

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On combining visual SLAM and dense scene flow to increase the robustness of localization and mapping in dynamic environments

Cited by 137 publications

References 23 publications

Enhancement Strategies for Frame-to-Frame Uas Stereo Visual Odometry

Enhancement Strategies for Frame-to-Frame Uas Stereo Visual Odometry

Visual Odometry by Multi-frame Feature Integration

Scaled monocular SLAM for walking people

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