Map based navigation is a crucial task for any mobile robot. On many platforms this problem is addressed by applying Simultaneous Localization and Mapping (SLAM) based on metric grid-maps. Such solutions work well on robots with adequate resources and limited workspaces. Platforms with limited payload which operate in unbounded workspaces, do often have insufficient resources to keep a metric world representation. Nevertheless, many applications demand that the robot can autonomously navigate between different operation areas. In this work the Landmark-Tree map (LT-map), a resource efficient topological map concept, is for the first time applied to a mobile robotic platform equipped with an omnidirectional camera. It enables the robot to efficiently adapt the acquired map online to the available memory. During map acquisition and navigation the motion is estimated by the Z∞-algorithm. Both methods are based on similar concepts, which results in a mutual benefit. An efficient navigation strategy based on the LT-map allows the robot to reliably follow previously recorded paths. The presented approach is evaluated on a mobile robot in indoor and outdoor scenarios. The experiments prove its feasibility and show that pruning the map just smooths the trajectories, which is the expected and desired behaviour.

Efficient visual pose estimation plays an important role for a variety of applications. To improve the quality, the measurements from different sensors can be fused. However, a reliable fusion requires the knowledge of the uncertainty of each estimate. In this work, we provide an error analysis for the Z∞algorithm. Furthermore, we extend the existing first-order error propagation for the 8-point algorithm to allow for feature normalization, as proposed by Hartley or Mühlich, and the rotation matrix based decomposition. Both methods are efficient visual odometry techniques which allow high frame-rates and, thus, dynamic motions in unbounded workspaces. Finally, we provide experiments which validate the accuracy of the error propagation and which enable a brief comparison, showing that the Z∞significantly outperforms the 8-point algorithm. We also discuss the influence of the number of features, the aperture angle, and the image resolution on the accuracy of the pose estimation.

This work aims at accurate estimation of the pose of a close-range 3-D modeling device in real-time, at high-rate, and solely from its own images. In doing so, we replace external positioning systems that constrain the system in size, mobility, accuracy, and cost. At close range, accurate pose tracking from image features is hard because feature projections do not only drift in the face of rotation but also in the face of translation. Large, unknown feature drifts may impede real-time feature tracking and subsequent pose estimation-especially with concurrent operation of other 3-D sensors on the same computer. The problem is solved in Ref. [1] by the partial integration of readings from a backing inertial measurement unit (IMU). In this work we avoid using an IMU by improved feature matching: full utilization of the current state estimation (including structure) during feature matching enables decisive modifications of the matching parameters for more efficient tracking-we hereby follow the Active Matching paradigm.

Inertia-visual sensor fusion has become popular due to the complementary characteristics of cameras and IMUs. Once the spatial and temporal alignment between the sensors is known, the fusion of measurements of these devices is straightforward. Determining the alignment, however, is a challenging problem. Especially the spatial translation estimation has turned out to be difficult, mainly due to limitations of camera dynamics and noisy accelerometer measurements. Up to now, filtering-based approaches for this calibration problem are largely prevalent. However, we are not convinced that calibration, as an offline step, is necessarily a filtering issue, and we explore the benefits of interpreting it as a batch-optimization problem. To this end, we show how to model the IMU-camera calibration problem in a nonlinear optimization framework by modeling the sensors' trajectory, and we present experiments comparing this approach to filtering and system identification techniques. The results are based both on simulated and real data, showing that our approach compares favorably to conventional methods.

Image-based scene representations enable a mobile robot to make a realistic prediction of its environment. Hence, it is able to rapidly detect changes in its surroundings by comparing a virtual image generated from previously acquired reference images and its current observation. This facilitates attentional control to novel events. However, illumination effects can impair attentional control if the robot does not take them into account. To address this issue, we present in this paper an approach for the acquisition of illumination-invariant scene representations. Using multiple spatial image sequences which are captured under varying illumination conditions the robot computes an illumination-invariant image-based environment model. With this representation and statistical models about the illumination behavior, the robot is able to robustly detect texture changes in its environment under different lighting. Experimental results show high-quality images which are free of illumination effects as well as more robust novelty detection compared to state-of-the-art methods.

One important feature of a cognitive system is to perceive and understand its environment and to adapt its actions to changes and unforeseen situations. In this paper, we propose a scheme for visual surprise detection in cognitive mobile robots. With the robot's observation and a set of reference images previously acquired near its current viewpoint, a pixel-wise surprise trigger is computed using Bayesian probabilistic inference techniques. With appropriate mathematical approximations this algorithm can be implemented on modern graphics hardware which nearly allows for real-time surprise detection. In order to refer to prior observations, a mobile robot has to be able to re-localize itself with respect to its environment. Thus, we also present two online image-based homing algorithms which both facilitate the computation of location-independent surprise triggers. Experiments show acceptable results in terms of robust and fast detection of unexpected changes in the environment.

Accurate online localization is crucial for mobile robotics. In this paper, we describe a real-time image-based localization technique, which is based on a single calibrated camera. This can be supported by a second camera to improve accuracy and to provide the correct translational scale. Our goal is a robust and unbiased pose estimation in highly dynamic scenes on resource-limited systems. The presented approach is characterized through significantly improved robustness of the pose estimation, a novel approach for stereo subpixel accurate landmark initialization, and the speed-up of conventional tracking routines to achieve online capability. Although the algorithm is designed for accurate, online short-range egomotion estimation in hand-held scanning devices, it can be used for any mobile robot application as shown in this paper. Various tests and experimental results with a mobile platform and a hand-held 3D modeler are presented and discussed.

In the context of 3-D scene modeling, this work aims at the accurate estimation of the pose of a close-range 3-D modeling device, in real-time and passively from its own images. This novel development makes it possible to abandon using inconvenient, expensive external positioning systems. The approach comprises an ego-motion algorithm tracking natural, distinctive features, concurrently with customary 3-D modeling of the scene. The use of stereo vision, an inertial measurement unit, and robust cost functions for pose estimation further increases performance. Demonstrations and abundant video material validate the approach.