Robust egomotion estimation using ICP in inverse depth coordinates

Lui, Wen Lik Dennis; Tang, Titus; Drummond, Tom; Li, Wai Ho

doi:10.1109/icra.2012.6225354

Cited by 15 publications

(15 citation statements)

References 21 publications

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“…Their work also included a surfel representation of the environment to have a more compact representation of the information available. A robust ICP flavour was experimented in (Lui et al, 2012) using an inverse depth parameterisation. Other related works merging photometric and geometric information for Visual Odometry (VO) can be found in (Tykkälä et al, 2011), (Tykkälä et al, 2013), (Kerl et al, 2013).…”

Section: Visual Odometrymentioning

confidence: 99%

A Dense Map Building Approach from Spherical RGBD Images

Gokhool¹,

Meilland

Rives³

et al. 2014

Proceedings of the 9th International Conference on Computer Vision Theory and Applications

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Abstract:Visual mapping is a required capability for practical autonomous mobile robots where there exists a growing industry with applications ranging from the service to industrial sectors. Prior to map building, Visual Odometry(VO) is an essential step required in the process of pose graph construction. In this work, we first propose to tackle the pose estimation problem by using both photometric and geometric information in a direct RGBD image registration method. Secondly, the mapping problem is tackled with a pose graph representation, whereby, given a database of augmented visual spheres, a travelled trajectory with redundant information is pruned out to a skeletal pose graph. Both methods are evaluated with data acquired with a recently proposed omnidirectional RGBD sensor for indoor environments.

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Section: Visual Odometrymentioning

confidence: 99%

A Dense Map Building Approach from Spherical RGBD Images

Gokhool¹,

Meilland

Rives³

et al. 2014

Proceedings of the 9th International Conference on Computer Vision Theory and Applications

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“…In this method, computer vision algorithms [7], [8], [9] are used to estimate a 6 DoF pose of a moving camera frame by analyzing a sequence of video frames. It is primarily tracking visual features from one video frame to another and instantaneously determining the camera pose.…”

Section: Introductionmentioning

confidence: 99%

Design, implementation and evaluation of a motion control scheme for mobile platforms with high uncertainties

Mashali

Alqasemi

Sarkar

et al. 2014

5th IEEE RAS/EMBS International Conference on Biomedical Robotics and Biomechatronics

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In this work, we present a motion control scheme for a robotic mobile platform using low-cost vision sensor to update encoder values. We track the pose of a power wheelchair using wheel encoders along with a Microsoft Kinect camera. Two methods of pose estimation are implemented and tested. These methods are a) encoder-based odometry and b)ICP(Iterative Closest Point)-based updated odometry. We evaluate the performance of each method using precise wheelchair pose ground truth data acquired via a state-of-theart VICON R system with eight motion capture cameras. Offline data processing is performed to refine the ICP parameters and estimate the covariance matrices of the Kalman filter. The offline data processing results demonstrate that our ICP-based updated odometry has very accurate pose tracking. By implementing our control scheme, the position error is improved by a factor of 15 and the localization orientation error is improved by a factor of 13. In online implementation, there was 4 times improvement for both position and orientation angle estimation. To demonstrate the robustness of our approach, we apply it for online obstacle avoidance. A wheelchair-mounted robotic arm (WMRA) is also included in this platform and will be used for future work on combined mobility and manipulation control with sensor assistance.

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“…For SIFT, FAST, and BRIEF, we rely on their implementations found in OpenCV 1 . For Harris and NARF their implementations in PCL 2 are used.…”

Section: Methodsmentioning

confidence: 99%

“…There already is some interesting work on fast registration for egomotion estimation using depth images [1], but the still missing component towards a full SLAM system using depth images is loop closure detection.…”

Section: Introductionmentioning

confidence: 99%

Loop closure detection using depth images

Scherer

Kloss

Zell

2013

2013 European Conference on Mobile Robots

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Abstract-We investigate the question whether loop closure detection using depth images is feasible using currently available depth features. For this reason, we collected a benchmark dataset consisting of a total number of 15 logfiles with several loops in various environments, implemented a modular and easily extensible loop closure detector and used this to evaluate the adequacy of state-of-the art depth features on our benchmark dataset. To allow for a fair comparison, we determined the best values for the sometimes large number of user-chosen parameters using a large-scale grid search. Since our benchmark dataset contains both depth and RGB images, we can compare the performance relying on depth features with the performance achieved when using intensity image features.

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Robust egomotion estimation using ICP in inverse depth coordinates

Cited by 15 publications

References 21 publications

A Dense Map Building Approach from Spherical RGBD Images

A Dense Map Building Approach from Spherical RGBD Images

Design, implementation and evaluation of a motion control scheme for mobile platforms with high uncertainties

Loop closure detection using depth images

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