Monocular SLAM for Visual Odometry
Catalonia Univ., Barcelona
DOI: 10.1109/WISP.2007.4447564 Conference: Intelligent Signal Processing, 2007. WISP 2007. IEEE International Symposium on
The ego-motion online estimation process from a video input is often called visual odometry. Typically optical flow and structure from motion (SFM) techniques have been used for visual odometry. Monocular simultaneous localization and mapping (SLAM) techniques implicitly estimate camera ego-motion while incrementally build a map of the environment. However in monocular SLAM, when the number of features in the system state increases, the computational cost grows rapidly; consequently maintaining frame rate operation becomes impractical. In this paper monocular SLAM is proposed for map-based visual odometry. The number of features is bounded removing features dynamically from the system state, for maintaining a stable processing time. In the other hand if features are removed then previous visited sites can not be recognized, nevertheless in an odometry context this could not be a problem. A method for feature initialization and a simple method for recovery metric scale are proposed. The experimental results using real image sequences show that the scheme presented in this paper is promising.
Available from: Antoni Grau
- "La búsqueda de las características se limita a regiones suscritas alrededor de la predicción h i y con área definida por la matriz de innovación (30) donde H i es el Jacobiano del modelo de observación con respecto al estado x̂ , P k es la matriz de covariancias a priori del estado. Se suponen mediciones corrompidas por ruido Gaussiano con media cero y covarianza R. Si desea más detalles acerca de la adaptación del método MTE para su aplicación en SLAM monocular, consulte Munguía y Grau (2007). Con el fin de validar experimentalmente el método MTE (en su variante de aplicación al SLAM "
[Show abstract] [Hide abstract]
ABSTRACT: The SLAM or Simultaneous Localization and Mapping, is a technique in which a robot or autonomous vehicle operate in an a priori unknown environment, using only its onboard sensors to simultaneously build a map of its surroundings and use it to track its position. The sensors have a large impact on the algorithm used for SLAM. Recent approaches are focusing on the use of cameras as the main sensor, because they yield a lot of information and are well adapted for embedded systems: they are light, cheap and power saving. However, unlike range sensors which provide range and angular information, a camera is a projective sensor which measures the bearing of images features. Therefore depth information (range) cannot be obtained in a single step. This fact has propitiated the emergence of a new family of SLAM algorithms: the Bearing-Only SLAM methods, which mainly rely in especial techniques for features system-initialization in order to enable the use of bearing sensors (as cameras) in SLAM systems. In this work a practical method is presented, for initializing new features in bearing-only SLAM systems. The proposed method, defines a single hypothesis for the initial depth of features, by the use of an stochastic technique of triangulation. Several simulations as well two scenarios of applications with real data are presented, in order to show the performance of the proposed method.
06/2013; 14(2):257-274. DOI:10.1016/S1405-7743(13)72241-8
Available from: Matthias Kranz
- "Various approaches to resolve the scale ambiguity and to establish a metric coordinate system have been proposed. For example, Davison et al.  add a calibration object with known size to the scene, or Munguia et al.  require manual selection of three scene points with known distance. Likewise, if objects with known size can be identified, they can be used to determine the metric scale. "
[Show abstract] [Hide abstract]
ABSTRACT: We present a visual odometry system for indoor navigation with a focus on long-term robustness and consistency. As our work is targeting mobile phones, we employ monocular SLAM to jointly estimate a local map and the device's trajectory. We specifically address the problem of estimating the scale factor of both, the map and the trajectory. State-of-the-art solutions approach this problem with an Extended Kalman Filter (EKF), which estimates the scale by fusing inertial and visual data, but strongly relies on good initialization and takes time to converge. Each visual tracking failure introduces a new arbitrary scale factor, forcing the filter to re-converge. We propose a fast and robust method for scale initialization that exploits basic geometric properties of the learned local map. Using random projections, we efficiently compute geometric properties from the feature point cloud produced by the visual SLAM system. From these properties (e.g., corridor width or height) we estimate scale changes caused by tracking failures and update the EKF accordingly. As a result, previously achieved convergence is preserved despite re-initializations of the map. To minimize the time required to continue tracking after failure, we perform recovery and re-initialization in parallel. This increases the time available for recovery and hence the likelihood for success, thus allowing almost seamless tracking. Moreover, fewer re-initializations are necessary. We evaluate our approach using extensive and diverse indoor datasets. Results demonstrate that errors and convergence times for scale estimation are considerably reduced, thus ensuring consistent and accurate scale estimation. This enables long-term odometry despite of tracking failures which are inevitable in realistic scenarios.
2012 International Conference on Indoor Positioning and Indoor Navigation (IPIN); 11/2012
Available from: Mohd Yamani Idna Idris
- "The Extended Kalman Filter (EKF) is one of the most commonly used estimation technique in Monocular SLAM. Though EKF can be executed at a lower computational cost, the computational cost of a Monocular SLAM grows when the number of features in the system state increases . For that reason, the EKF algorithm performance issues have been studied by many researchers. "
[Show abstract] [Hide abstract]
ABSTRACT: Extended Kalman Filter (EKF) is a non-linear state estimation technique which is used to produce values that close to the true value when given with measurement containing noise and other inaccuracies. In Monocular Simultaneous Localization and Mapping (SLAM), EKF is used to estimate position and motion information. In this paper, Monocular SLAM software implementation on a general purpose computer is studied to find the most time consuming part of the estimation program. The analysis concentrates on the Monocular SLAM EKF estimation process which involves prediction, measurement prediction, matching and update. For this purpose, a form of dynamic programming analysis tool called software profiling is utilized to determine which section of the estimation program demands the highest processing time. Based on the analysis, it is found that EKF “matching” process contribute to the highest computation time. The reason behind the time-consuming process is because for every predicted feature in the matching stage, the acceptance region and their cross correlation have to be calculated. In a typical general purpose computer software implementation, the processing is limited to sequences of operations (i.e. sequential processing). Such implementation will delay the next process until the prior process completed. However, further analysis conducted in this paper shows that each feature does not depend on the prior process and can be processed individually. This would allow several features to be processed simultaneously to improve the execution speed. Therefore, an FPGA pipelined and parallel processing architecture is proposed.
Measurement 10/2012; 45(8):2141–2152. DOI:10.1016/j.measurement.2012.05.018 · 1.48 Impact Factor
Data provided are for informational purposes only. Although carefully collected, accuracy cannot be guaranteed. The impact factor represents a rough estimation of the journal's impact factor and does not reflect the actual current impact factor. Publisher conditions are provided by RoMEO. Differing provisions from the publisher's actual policy or licence agreement may be applicable.