Validation of Data Association for Monocular SLAM

Guerra, Edmundo; Munguía, Rodrigo; Bolea, Yolanda; Grau, Antoni

doi:10.1155/2013/671376

Cited by 7 publications

(13 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The above drawbacks can be mitigated by the use of batch validation techniques [34,35]. Nevertheless, this kind of technique commonly adds extra computational time.…”

Section: Methods Descriptionmentioning

confidence: 99%

A Robust Approach for a Filter-Based Monocular Simultaneous Localization and Mapping (SLAM) System

Munguía

Castillo–Toledo

Grau

2013

Sensors

Self Cite

View full text Add to dashboard Cite

Simultaneous localization and mapping (SLAM) is an important problem to solve in robotics theory in order to build truly autonomous mobile robots. This work presents a novel method for implementing a SLAM system based on a single camera sensor. The SLAM with a single camera, or monocular SLAM, is probably one of the most complex SLAM variants. In this case, a single camera, which is freely moving through its environment, represents the sole sensor input to the system. The sensors have a large impact on the algorithm used for SLAM. Cameras are used more frequently, because they provide a lot of information and are well adapted for embedded systems: they are light, cheap and power-saving. Nevertheless, and unlike range sensors, which provide range and angular information, a camera is a projective sensor providing only angular measurements of image features. Therefore, depth information (range) cannot be obtained in a single step. In this case, special techniques for feature system-initialization are needed in order to enable the use of angular sensors (as cameras) in SLAM systems. The main contribution of this work is to present a novel and robust scheme for incorporating and measuring visual features in filtering-based monocular SLAM systems. The proposed method is based in a two-step technique, which is intended to exploit all the information available in angular measurements. Unlike previous schemes, the values of parameters used by the initialization technique are derived directly from the sensor characteristics, thus simplifying the tuning of the system. The experimental results show that the proposed method surpasses the performance of previous schemes.

show abstract

“…The above drawbacks can be mitigated by the use of batch validation techniques [34,35]. Nevertheless, this kind of technique commonly adds extra computational time.…”

Section: Methods Descriptionmentioning

confidence: 99%

A Robust Approach for a Filter-Based Monocular Simultaneous Localization and Mapping (SLAM) System

Munguía

Castillo–Toledo

Grau

2013

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…Although some degree of validation is provided by the above technique, more robustness for the data association process can be obtained by the use of batch validation techniques as [26][27][28]. These techniques are applied during the update stage of the filter.…”

Section: Detection and Tracking Of Visual Measurementsmentioning

confidence: 99%

Method for SLAM Based on Omnidirectional Vision: A Delayed-EKF Approach

et al. 2017

View full text Add to dashboard Cite

This work presents a method for implementing a visual-based simultaneous localization and mapping (SLAM) system using omnidirectional vision data, with application to autonomous mobile robots. In SLAM, a mobile robot operates in an unknown environment using only on-board sensors to simultaneously build a map of its surroundings, which it uses to track its position. The SLAM is perhaps one of the most fundamental problems to solve in robotics to build mobile robots truly autonomous. The visual sensor used in this work is an omnidirectional vision sensor; this sensor provides a wide field of view which is advantageous in a mobile robot in an autonomous navigation task. Since the visual sensor used in this work is monocular, a method to recover the depth of the features is required. To estimate the unknown depth we propose a novel stochastic triangulation technique. The system proposed in this work can be applied to indoor or cluttered environments for performing visual-based navigation when GPS signal is not available. Experiments with synthetic and real data are presented in order to validate the proposal.

show abstract

“…The camera itself, as a sensor, is able to produce great amounts of data, which within the SLAM context can be used to deal with several of the key issues after processing the image data with computer vision techniques. In [ 6 , 7 ] several other sensors are discussed within the SLAM framework, comparing their strengths and weaknesses against the use of monocular cameras, beyond the availability and accessibility of such sensors.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, exploratory HRI opens the door to improve known mapping techniques exploiting the opportunities provided by the human component. With that end, an initial approach to monocular SLAM with collaborative perception based on HRI was presented in [ 23 ], where the delayed inverse depth monocular SLAM (DI-D SLAM) [ 6 ] with highest order hypothesis compatibility test (HOHCT) validation [ 7 ] was modified so that with the help of a secondary camera, the delayed feature initialization process could accept features with instantly estimated depth.…”

Section: Introductionmentioning

confidence: 99%

Human Collaborative Localization and Mapping in Indoor Environments with Non-Continuous Stereo

Guerra

Munguía

Bolea

et al. 2016

Sensors

Self Cite

View full text Add to dashboard Cite

A new approach to the monocular simultaneous localization and mapping (SLAM) problem is presented in this work. Data obtained from additional bearing-only sensors deployed as wearable devices is fully fused into an Extended Kalman Filter (EKF). The wearable device is introduced in the context of a collaborative task within a human-robot interaction (HRI) paradigm, including the SLAM problem. Thus, based on the delayed inverse-depth feature initialization (DI-D) SLAM, data from the camera deployed on the human, capturing his/her field of view, is used to enhance the depth estimation of the robotic monocular sensor which maps and locates the device. The occurrence of overlapping between the views of both cameras is predicted through geometrical modelling, activating a pseudo-stereo methodology which allows to instantly measure the depth by stochastic triangulation of matched points found through SIFT/SURF. Experimental validation is provided through results from experiments, where real data is captured as synchronized sequences of video and other data (relative pose of secondary camera) and processed off-line. The sequences capture indoor trajectories representing the main challenges for a monocular SLAM approach, namely, singular trajectories and close turns with high angular velocities with respect to linear velocities.

show abstract

Validation of Data Association for Monocular SLAM

Cited by 7 publications

References 30 publications

A Robust Approach for a Filter-Based Monocular Simultaneous Localization and Mapping (SLAM) System

A Robust Approach for a Filter-Based Monocular Simultaneous Localization and Mapping (SLAM) System

Method for SLAM Based on Omnidirectional Vision: A Delayed-EKF Approach

Human Collaborative Localization and Mapping in Indoor Environments with Non-Continuous Stereo

Contact Info

Product

Resources

About