Previous work in simultaneous localisation and mapbuilding (SLAM) for mobile robots has focused on the simplified case in which a robot is considered to move in two dimensions on a ground plane. While this is sometimes a good approximation, a large number of real-world applications require robots to move around terrain which has significant slopes and undulations. In this paper we describe an EKFbased SLAM system permitting unconstrained 3D localisation, and in particular develop models for the motion of a wheeled robot in the presence of unknown slope variations. In a fully automatic implementation, our robot observes visual point features using fixating stereo vision and builds a sparse map on-the-fly. Combining this visual measurement with information from odometry and a roll/pitch accelerometer sensor, the robot performs accurate, repeatable localisation while traversing an undulating course. 1.

Abstract — When using appearance-based recognition for self-localization of mobile robots, the images obtained during the exploration of the environment need to be efficiently stored in the memory. PCA offers means for representing the images in a low-dimensional subspace, which allows for efficient matching and recognition. For active exploration it is necessary to use an incremental method for the computation of the subspace. While such methods have been considered before, only the on-line construction of eigenvectors has been addressed. Representations of the images in the subspace were computed only after the final subspace had been built, requiring that all the images were kept in the memory. In this paper we propose to use an incremental PCA algorithm with the updating of partial image representations in a way that allows the robot to discard the acquired images immediately after the update. Such a model is open-ended, meaning that we can easily update it with new images. We show that the performance of the proposed method is comparable to the performance of the batch method in terms of compression, computational cost and the precision of localization. We also show that by applying the repetitive learning, the subspace converges to that constructed with the batch method. Keywords—Robot localization, on-line visual learning, PCA updating, view-based robot localization, repetitive learning. I.

This communication addresses the issue of concurrent map building and localization (CML) for a mobile robot in an unknown environment. The proposed solution extends over previous contributions in that the environment must not be static, nor the landmarks be uniquely identifiable. To this aim we introduce a map model that includes not only the robot and landmark locations in a reference frame, but also a model for landmark quality assessment. Convergence of the map covariance is preserved in the new map model.

We examine the problem of minimizing uncertainty in the automated construction of a visual map of an unknown environment. Our work is motivated by the idea that a robot's exploration policy can impact the accuracy of the resulting map, and we seek to determine a policy that optimizes a trade-o# between accuracy and e#ciency. We are further motivated by the specific requirements of our map representation, which learns a set of implicit models of visual features. Such a representation precludes the instantiation of explicitly parameterized landmarks, such as those employed in standard concurrent mapping and localization frameworks. This paper examines a parameterized family of spiral trajectories and determines parameterizations that yield reliable maps. We present experimental results demonstrating the map construction framework and discuss the implications for future work.

This thesis addresses some key issues which affect the level of autonomy inherent in visual navigation systems, with wider applicability in a range of fields. They can be divided into two areas. Firstly, automated initialisation, in which the kinematic and camera calibration parameters needed for an active camera platform are calculated without user interaction. Secondly, the complexity problem of simultaneous localisation and mapping using the Extended Kalman Filter, which is a highly general and flexible localisation methodology for keeping track of the position of a vehicle as it navigates an unknown scene. The problem, which is inherent in any probabilistic mapping filter, is that the computational cost of incorporating new measurements scales with the size of the region being explored. Alignment is required for any active camera used to make measurement by way of angle encoders. This is the process of bringing each axis to a natural origin defined by an active head’s kinematics, and is necessary for referring measurements back to a fixed coordinate frame. In this thesis, alignment is achieved by detecting image-based invariants to motions about individual axes. A number of algorithms are developed for most typical scenarios,

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Abstract—The domain of vision and navigation often includes applications for feature tracking as well as simultaneous localization and mapping (SLAM). As these problems require computationally demanding solutions, it is challenging to achieve high performance without sacrificing the fidelity of results or otherwise consuming excessive amounts of energy. Our goal then is to accelerate the applications in this domain to meet real-time performance constraints while simultaneously reducing energy consumption and avoiding degradation in the quality of results. To achieve this domain-specific acceleration, we model a customizable hardware platform based on the 3D integration of a Field-Programmable Gate Array (FPGA) atop a standard chip multiprocessor (CMP) with Through-Silicon Vias (TSVs) used for communication between the two layers. Furthermore, partial automation of accelerator creation using C-to-RTL tools