The SLAM (Simultaneous Localization and Mapping) problem is one of the essential challenges for the current robotics. Our main objective in this work is to develop a real-time visual SLAM system using monocular omnidirectional vision. Our approach is based on the Extended Kalman Filter (EKF). We use the Spherical Camera Model to obtain geometric information from the images. This model is integrated in the EKF-based SLAM through the linearization of the direct and the inverse projections. We introduce a new computation of the descriptor patch for catadioptric omnidirectional cameras which aims to reach rotation and scale invariance. We perform experiments with omnidirectional images comparing this new approach with the conventional one. The experimentation confirms that our approach works better with omnidirectional cameras since features last longer and constructed maps are bigger.
Abstract-In this work we integrate the Spherical Camera Model for catadioptric systems in a Visual-SLAM application. The Spherical Camera Model is a projection model that unifies central catadioptric and conventional cameras. To integrate this model into the Extended Kalman Filter-based SLAM we require to linearize the direct and the inverse projection. We have performed an initial experimentation with omnidirectional and conventional real sequences including challenging trajectories. The results confirm that the omnidirectional camera gives much better orientation accuracy improving the estimated camera trajectory.
An important part of current research on appearance based mapping goes towards richer semantic representations of the environment, which may allow autonomous systems to perform higher level tasks and provide better human-robot interaction. This work presents a new omnidirectional vision based scene labeling approach for augmented indoor topological mapping. Omnidirectional vision systems are of particular interest because they allow us to have more compact and efficient representation of the environment. Our proposal includes novel ideas in order to augment the semantic information of a typical indoor topological map: we pay special attention to the semantic labels of the different types of transitions between places, and propose a simple way to include this semantic information to build a topological map, as part of the criteria to segment the environment. This work is built on efficient catadioptric image representation based on the Gist descriptor, which is used to classify the acquired views into types of indoor regions. The basic types of indoor regions considered are Place and Transition, farthest divided into more specific subclasses, e.g., Transition into door, stairs and elevator. Besides using the result of this labeling, the proposed mapping approach includes a probabilistic model to account for spatio-temporal consistency. All the proposed ideas have been evaluated in a new indoor dataset presented in this paper. This dataset has been acquired with our wearable catadioptric vision system 1 , showing promising results in a realistic prototype.
Autonomous navigation and recognition of the environment are fundamental abilities for people extensively studied in computer vision and robotics fields. Expansion of low cost wearable sensing provides interesting opportunities for assistance systems that augment people navigation and recognition capabilities. This work presents our wearable omnidirectional vision system and a novel twophase localization approach running on it. It runs stateof-the-art real time visual odometry adapted to catadioptric images augmented with topological-semantic information. The presented approach benefits from using wearable sensors to improve visual odometry results with true scaled solution. The wide field of view of catadioptric vision system used makes features last longer in the field of view and allows more compact location representation which facilitates topological place recognition. Experiments in this paper show promising ego-localization results in realistic settings, providing good true scaled visual odometry estimation and recognition of indoor regions.
Navigation is a challenging task for many travelers with visual impairments. While a variety of GPS-enabled tools can provide wayfinding assistance in outdoor settings, GPS provides no useful localization information indoors. A variety of indoor navigation tools are being developed, but most of them require potentially costly physical infrastructure to be installed and maintained, or else the creation of detailed visual models of the environment. We report development of a new smartphone-based navigation aid, which combines inertial sensing, computer vision and floor plan information to estimate the user's location with no additional physical infrastructure and requiring only the locations of signs relative to the floor plan. A formative study was conducted with three blind volunteer participants demonstrating the feasibility of the approach and highlighting the areas needing improvement.
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