Inverse depth for accurate photometric and geometric error minimisation in RGB-D dense visual odometry

Gutiérrez-Gómez, Daniel; Mayol-Cuevas, Walterio; Guerrero, J. J.

doi:10.1109/icra.2015.7138984

Cited by 29 publications

(17 citation statements)

References 40 publications

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“…This paper extends on our previous conference paper [12] which presented a direct visual odometry method minimising both types of error. Our main contribution is the novelty of using the inverse depth to parametrise the geometric error instead of the depth as most works do.…”

Section: Introductionmentioning

confidence: 57%

Dense RGB-D visual odometry using inverse depth

Gutiérrez-Gómez

Mayol-Cuevas

Guerrero

2016

Robotics and Autonomous Systems

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In this paper we present a dense visual odometry system for RGB-D cameras performing both photometric and geometric error minimisation to estimate the camera motion between frames. Contrary to most works in the literature, we parametrise the geometric error by the inverse depth instead of the depth, which translates into a better fit of the distribution of the geometric error to the used robust cost functions. To improve the accuracy we propose to use a keyframe switching strategy based on a visibility criteria between frames. For the comparison of our approach with state-of-the-art approaches we use the popular datasets from the TUM for RGB-D benchmarking as well as two synthetic datasets. Our approach shows to be competitive with state-of-theart methods in terms of drift in meters per second, even compared to methods performing loop closure too. When comparing to approaches performing pure odometry like ours, our method outperforms them in the majority of the tested datasets. Additionally we show that our approach is able to work in real time and we provide a qualitative evaluation on our own sequences showing a low drift in the 3D reconstructions. We have implemented this method within the scope of PCL (Point Cloud Library) as a branch of the code for large scale KinectFusion, where the original ICP system for odometry estimation has been completely substituted by our method. A PCL fork including the modified method is available for download .

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Section: Introductionmentioning

confidence: 57%

Dense RGB-D visual odometry using inverse depth

Gutiérrez-Gómez

Mayol-Cuevas

Guerrero

2016

Robotics and Autonomous Systems

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“…[25] scale each depth error using its squared inverse depth. [15] proposes to use the inverse depth in the minimization of the geometric reprojection error. We evaluate this parametrization in our system.…”

Section: Related Workmentioning

confidence: 99%

RGBDTAM: A cost-effective and accurate RGB-D tracking and mapping system

Concha

Civera

2017

2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Abstract-Simultaneous Localization and Mapping using RGB-D cameras has been a fertile research topic in the latest decade, due to the suitability of such sensors for indoor robotics. In this paper we propose a direct RGB-D SLAM algorithm with state-of-the-art accuracy and robustness at a los cost. Our experiments in the RGB-D TUM dataset [34] effectively show a better accuracy and robustness in CPU real time than direct RGB-D SLAM systems that make use of the GPU.The key ingredients of our approach are mainly two. Firstly, the combination of a semi-dense photometric and dense geometric error for the pose tracking (see Figure 1), which we demonstrate to be the most accurate alternative. And secondly, a model of the multi-view constraints and their errors in the mapping and tracking threads, which adds extra information over other approaches. We release the open-source implementation of our approach 1 . The reader is referred to a video with our results 2 for a more illustrative visualization of its performance.

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“…For the visual odometry from RGB-D there are many approaches [33,34]. We use the method presented by Gutierrez-Gomez et al [7], where visual odometry is obtained in real time from the dense RGB and inverse depth maps by establishing pixel-wise constraints through the flow equations.…”

Section: Visual Odometry Modulementioning

confidence: 99%

“…This stair detection method is thought to be part of a more general personal assistant based on computer vision which includes other applications such as obstacle detection and audio interface [5,6]. In our current framework, we have included the estimation of the visual odometry from [7] during the navigation in order to maintain location awareness and to know the relative position to relevant features in the scene even when they are not in the current view.…”

Section: Introductionmentioning

confidence: 99%

Stairs detection with odometry-aided traversal from a wearable RGB-D camera

Pérez-Yus

Gutiérrez-Gómez

López‐Nicolás

et al. 2017

Computer Vision and Image Understanding

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Stairs are one of the most common structures present in human-made scenarios, but also one of the most dangerous for those with vision problems. In this work we propose a complete method to detect, locate and parametrise stairs with a wearable RGB-D camera. Our algorithm uses the depth data to determine if the horizontal planes in the scene are valid steps of a staircase judging their dimensions and relative positions. As a result we obtain a scaled model of the staircase with the spatial location and orientation with respect to the subject. The visual odometry is also estimated to continuously recover the current position and orientation of the user while moving. This enhances the system giving the ability to come back to previously detected features and providing location awareness of the user during the climb. Simultaneously, the detection of the staircase during the traversal is used to correct the drift of the visual odometry. A comparison of results of the stair detection with other state-of-the-art algorithms was performed using public dataset. Additional experiments have also been carried out, recording our own natural scenes with a chest-mounted RGB-D camera in indoor scenarios. The algorithm is robust enough to work in real-time and even under partial occlusions of the stair.

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Inverse depth for accurate photometric and geometric error minimisation in RGB-D dense visual odometry

Cited by 29 publications

References 40 publications

Dense RGB-D visual odometry using inverse depth

Dense RGB-D visual odometry using inverse depth

RGBDTAM: A cost-effective and accurate RGB-D tracking and mapping system

Stairs detection with odometry-aided traversal from a wearable RGB-D camera

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