The present work explores the link between navigational processes and the experience of place by considering the case of Evenki reindeer herders and hunters. Our analysis shows how the idiosyncratic wayfinding methods of the Evenki result in a unique experience of place – a case that elucidates the important question of the impact of navigational processes on environmental experience, and that advances the debate between mental map theory and practical mastery theory in anthropology. We defend that their wayfinding methods – involving a particular gait, path networks, and vast hydrological and toponymical knowledge – allow the Evenki to navigate without a need for integrating egocentric and allocentric frames of reference. As a result, the Evenki experience themselves as free individuals moving through an environment that is alive and rife with possibility. This analysis reveals the ways in which wayfinding processes relying predominantly on route knowledge – as opposed to survey knowledge – affect environmental experience. Alternative methods of wayfinding can be seen as a form of resistance to the uniformisation of landscapes, and as a way of embracing the heterogeneity of space.

The paper offers an analysis of pied-piping within the theoretical framework of Word Grammar. This framework combines cognitive linguistics with dependency grammar, so it assumes that the full power of domain-general cognition is available for syntax, and that syntactic structure can be conceived as a network of relations between individual words. In this network, words are related by at least two kinds of link: dependencies and ‘landmark’ links that determine word order. To handle the special characteristics of pied-piping, the analysis also includes a single special relation, ‘pipee’, which links the ‘piper’ (the wh-type word) to the word which replaces it in the landmark structure. The analysis is applied in detail to English, and then compared with previous analyses and extended to accommodate both the pied-piping with inversion found in Meso-American languages, and the boundary markers found in other languages.

This paper proposes a globally and exponentially convergent predictive observer for attitude and position estimation based on landmark measurements and velocity (angular and linear) readings. It is assumed that landmark measurements are available with time-delay. The maximum value of the sensor delay under which the estimation error converges to zero is calculated. Synthesis of the observer is based on a representation of rigid-body kinematics and sensor delay, formulated via ordinary and partial differential equations (ODE-PDE). Observability condition specifies necessary and sufficient landmark configuration for convergence of attitude and position estimation error to zero. Finally, for implementation purposes, a PDE-free realization of the predictive observer is proposed. Simulation results are presented to demonstrate performance and convergence properties of the predictive observer in case of a wheeled mobile robot.

Many navigation services, such as car navigation services, provide users with praxic navigational instructions (such as “turn left after 200 metres, then turn right after 150 metres”), however people usually associate directions with visual cues (e.g. “turn right at the square”) when giving navigational instructions in their daily conversations. Landmarks can play an equally important role in navigation and routing services. Landmarks are unique and easy-to-recognise and remember features; therefore, in order to remember when exploring an unfamiliar environment, they would be assets. In addition, Landmarks can be found both indoors and outdoors and their locations are usually fixed. Any positioning techniques which use landmarks as reference points can potentially provide seamless (indoor and outdoor) positioning solutions. For example, users can be localised with respect to landmarks if they can take a photograph of a registered landmark and use an application for image processing and feature extraction to identify the landmark and its location. Landmarks can also be used in pedestrian-specific path finding services. Landmarks can be considered as an important parameter in a path finding algorithm to calculate a route passing more landmarks (to make the user visit a more tourist-focussed area, pass along an easier-to-follow route, etc.). Landmarks can also be used as a part of the navigational instructions provided to users; a landmark-based navigation service makes users sure that they are on the correct route, as the user is reassured by seeing the landmark whose information/picture has just been provided as a part of navigational instruction. This paper shows how landmarks can help improve positioning and praxic navigational instructions in all these ways.

Elbows and branches in pipelines produce unique image patterns, which can be used as landmarks for autonomous navigation inside the pipelines. This paper presents two landmark detection methods, known as shadow-based method and laser projection method. The first method uses the landmark's unique patterns of shadow produced by the robot's illuminator. The other method exploits special line features generated by its own line-laser beam projector. The basic algorithms for extracting the landmarks are given and special sensor mechanisms are addressed respectively. Finally, the detection performances of each method are validated in various pipeline conditions by using an in-pipe robot, called MRINSPECT-V.

Antheraea assamensis (Helfer, 1837) is a sericigenous insect with distinct coloration, namely green, blue, orange and yellow in their larval stages and these are multivoltine in culture. However, wild morphs with diapause are found in dense forests. In this study, the wing venation and its variation in A. assamensis were analysed using traditional as well as landmark-based morphometric methods. The venation patterns are reviewed and significant changes in veins recorded. The samples were analysed using geometric morphometric methods and other conventional statistical methods. Landmark-based data were measured among selected veins and the centroid sizes, procrustes to tangent distance of different veins were analysed. Results show significant differences in some of the veins among the intraspecies of A. assamensis. The results suggest that both visual comparison as well as a quantitative approach in A. assamensis may lead to unsound taxonomic conclusions at intraspecies level.

For the navigation of a mobile robot, the determination of its absolute location is one of essential tasks. This paper proposes a stripe type landmark which enables us to develop a fast algorithm to calculate the distance and orientation of a camera relative to the landmark. Exact and closed form solution of the camera location is obtained from the geometric relation between the pattern of landmark and its projected image. Since the presented algorithm requires only one row of the mark image, determination of the location can be processed in real-time. In addition, the mark can be identified using the projective invariant of the original mark pattern without any additional patterns. The effect of the error sources on the location determination is analyzed through a series of simulations and experiments. The applicability of the algorithm to mobile robots is discussed using the experimental results.

This paper presents an absolute position estimation system for a mobile robot moving on a flat surface. In this system, a 3-D landmark with four coplanar points and a non-coplanar point is utilized to improve the accuracy of position estimation and to guide the robot during navigation. Applying theoretical analysis, we investigate the image sensitivity of the proposed 3-D landmark compared with the conventional 2-D landmark. In the camera calibration stage of the experiments, we employ a neural network as a computational tool. The neural network is trained from a set of learning data collected at various points around the mark so that the extrinsic and intrinsic parameters of the camera system can be resolved. The overall estimation algorithm from the mark identification to the position determination is implemented in a 32-bit personal computer with an image digitizer and an arithmetic accelerator. To demonstrate the effectiveness of the proposed 3-D landmark and the neural network-based calibration scheme, a series of navigation experiments were performed on a wheeled mobile robot (LCAR) in an indoor environment. The results show the feasibility of the position estimation system applicable to mobile robot's real-time navigation.