Inverse depth to depth conversion for monocular slam (original) (raw)

Unified inverse depth parametrization for monocular SLAM

2006

Abstract—Recent work has shown that the probabilistic SLAM approach of explicit uncertainty propagation can succeed in permitting repeatable 3D real-time localization and mapping even in the 'pure vision'domain of a single agile camera with no extra sensing. An issue which has caused difficulty in monocular SLAM however is the initialization of features, since information from multiple images acquired during motion must be combined to achieve accurate depth estimates.

Monocular SLAM with Inverse Scaling Parametrization

2008

The recent literature has shown that it is possible to solve the monocular Simultaneous Localization And Mapping using both undelayed features initialization and an Extedend Kalman Filter. The key concept, to achieve this result, was the introduction of a new parametrization called Unified Inverse Depth that produces measurements equations with a high degree of linearity and allows an efficient and accurate modeling of uncertainties. In this paper we present a monocular EKF SLAM filter based on an alternative parametrization, i.e., the Inverse Scaling Parametrization, characterized by a reduced number of parameters, a more linear measurement model, and a better modeling of features uncertainty for both low and high parallax features. Experiments in simulation demonstrate that the use of the Inverse Scaling solution improves the monocular EKF SLAM filter when compared with the Unified Inverse Depth approach, while experiments on real data show the system working as well.

Efficient Feature Parameterisation for Visual SLAM Using Inverse Depth Bundles

Procedings of the British Machine Vision Conference 2008, 2008

Flexibility and robustness of visual SLAM systems have been shown to benefit from an inverse depth parameterisation of features. However the increased number of 6 parameters per feature presents a problem to real-time EKF SLAM implementations because their computational complexity scales quadratically with the size of the state vector. Recent work tackles this for instance by converting the representation of well-established features from inverse to regular depth. In this paper, we propose a parameterisation where bundles of features share a common representation of the view-point they were initially observed from. According to the experiments performed, a feature occupies effectively about 1.5 state parameters in the proposed approach, allowing real-time performance for maps with more than 200 features.

On the use of inverse scaling in monocular SLAM

2009

Recent works have shown that it is possible to solve the Simultaneous Localization And Mapping problem using an Extended Kalman Filter and a single perspective camera. The principal drawback of these works is an inaccurate modeling of measurement uncertainties, which therefore causes inconsistencies in the filter estimations. A possible solution to proper uncertainty modeling is the Unified Inverse Depth parametrization. In this paper we propose the Inverse Scaling parametrization that still allows an un-delayed initialization of features, while reducing the number of needed parameters and simplifying the measurement model. This novel approach allows a better uncertainty modeling of both low and high parallax features and reduces the likelihood of inconsistencies. Experiments in simulation demonstrate that the use of the Inverse Scaling solution improves the performance of the monocular EKF SLAM filter when compared with the Unified Inverse Depth approach; experiment on real data confirm the applicability of the idea.

Delayed Features Initialization for Inverse Depth Monocular SLAM

2007

Recently, the unified inverse depth parametrization has shown to be a good option for challenging monocular SLAM problem, in a scheme of EKF for the estimation of the stochastic map and camera pose. In the original approach, features are initialized in the first frame observed (undelayed initialization), this aspect has advantages but also some problems. In this paper a delayed feature initialization is proposed for adding new features to the stochastic map. The results show that delayed initialization can improve some aspects without losing the performance and unified aspect of the original method, when initial reference points are used in order to fix a metric scale in the map.

Delayed Inverse Depth Monocular SLAM

Proceedings of the 17th IFAC World Congress, 2008, 2008

The 6-DOF monocular camera case possibly represents the harder variant in the context of simultaneous localization and mapping problem. In the last years, several advances have been appeared in this area; however the application of these techniques to real world applications it's difficult so far. Recently, the unified inverse depth parametrization has shown to be a good option this challenging problem, in a scheme of EKF for the estimation of the stochastic map and camera pose. In this paper a new delayed initialization scheme is proposed for adding new features to the stochastic map. The results show that delayed initialization can improve some aspects without losing the performance and unified aspect of the original method, when initial reference points are used in order to fix a metric scale in the map.

Inertial aiding of inverse depth SLAM using a monocular camera

Proceedings of the 2007 Ieee International Conference on Robotics and Automation, Vols 1-10, 2007

This paper presents the benefits of using a low cost inertial measurement unit to aid in an implementation of inverse depth initialized SLAM using a hand-held monocular camera. Results are presented with and without inertial observations for different assumed initial ranges to features on the same dataset. When using only the camera, the scale of the scene is not observable. As expected, the scale of the map depends on the prior used to initialize the depth of the features and may drift when exploring new terrain, precluding loop closure. The results show that the inertial observations help to improve the estimated trajectory of the camera leading to a better estimation of the map scale and a more accurate localization of features.

Article Monocular SLAM for Autonomous Robots with Enhanced Features Initialization

2014

This work presents a variant approach to the monocular SLAM problem focused in exploiting the advantages of a human-robot interaction (HRI) framework. Based upon the delayed inverse-depth feature initialization SLAM (DI-D SLAM), a known monocular technique, several but crucial modifications are introduced taking advantage of data from a secondary monocular sensor, assuming that this second camera is worn by a human. The human explores an unknown environment with the robot, and when their fields of view coincide, the cameras are considered a pseudo-calibrated stereo rig to produce estimations for depth through parallax. These depth estimations are used to solve a related problem with DID monocular SLAM, namely, the requirement of a metric scale initialization through known artificial landmarks. The same process is used to improve the performance of the technique when introducing new landmarks into the map. The convenience of the approach taken to the stereo estimation, based on SURF features matching, is discussed. Experimental validation is provided through results from real data with results showing the improvements in terms of more features correctly initialized, with reduced uncertainty, thus reducing scale and orientation drift. Additional discussion in terms of how a real-time implementation could take advantage of this approach is provided.

Monocular SLAM for Autonomous Robots with Enhanced Features Initialization

Sensors, 2014

This work presents a variant approach to the monocular SLAM problem focused in exploiting the advantages of a human-robot interaction (HRI) framework. Based upon the delayed inverse-depth feature initialization SLAM (DI-D SLAM), a known monocular technique, several but crucial modifications are introduced taking advantage of data from a secondary monocular sensor, assuming that this second camera is worn by a human. The human explores an unknown environment with the robot, and when their fields of view coincide, the cameras are considered a pseudo-calibrated stereo rig to produce estimations for depth through parallax. These depth estimations are used to solve a related problem with DI-D monocular SLAM, namely, the requirement of a metric scale initialization through known artificial landmarks. The same process is used to improve the performance of the technique when introducing new landmarks into the map. The convenience of the approach taken to the stereo estimation, based on SURF features matching, is discussed. Experimental validation is provided through results from real data with results showing the improvements in terms of more features correctly initialized, with reduced uncertainty, thus reducing scale and orientation drift. Additional discussion in terms of how a real-time implementation could take advantage of this approach is provided.

Experimental verification of direct depth computing technique for monocular visual SLAM systems

2012 First International Conference on Innovative Engineering Systems, 2012

this paper verifies a recently published method of monocular depth computing in the context of visual SLAM. The closed form depth solution was exploited in the measurement model of a monocular EKF visual SLAM algorithm. SIFT interest points are tracked during camera motion and a suitable feature initialization is presented. The visual SLAM system is verified through experiments on a mobile robot platform and the results are benchmarked to groundtruth.