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The purpose of this paper is to improve decision quality, and therefore project and portfolio success, by testing the influence of different visual representations of interdependency data in a simulated decision experiment. A network mapping approach to visualize project interdependencies is introduced and compared with matrix and tabular displays.

A simulated decision task in a controlled classroom setting tested five hypotheses though a sample of 480 experiments.

The type of data representation used is associated with differing levels of decision quality, and the use of network mapping displays is aligned with the best results.

The findings are limited as this experiment-based study presented a simplified decision scenario and involved students rather than practicing managers. The findings are best interpreted in combination with organization-based research.

The findings of this study suggest that visual data displays, particularly network mapping displays, can provide benefits and improve project portfolio decision quality. Managers may draw upon this study to design ways to include visual data representations in their project portfolio management decision processes.

This study uses experimentation to complement organization-based studies to better understand the influence of different methods of visualizing data and managing interdependencies between projects. This research provides an important contribution to meet the acknowledged need for better tools to understand and manage project interdependencies.

The aim of this paper is to frame the stakeholder-driven system mapping approach in the context of climate change, building on stakeholder knowledge of system boundaries, key elements and interactions within a system and to introduce a decision support tool for managing and visualising this knowledge into insightful system maps with policy implications.

This methodological framework is based on the concepts of market maps. The process of eliciting and visualising expert knowledge is facilitated by means of a reference implementation in MATLAB, which allows for designing technological innovation systems models in either a structured or a visual format.

System mapping can contribute to evaluating systems for climate change by capturing knowledge of expert groups with regard to the dynamic interrelations between climate policy strategies and other system components, which may promote or hinder the desired transition to low carbon societies.

This study explores how system mapping addresses gaps in analytical tools and complements the systems of innovation framework. Knowledge elicitation, however, must be facilitated and build upon a structured framework such as technological innovation systems.

This approach can provide policymakers with significant insight into the strengths and weaknesses of current policy frameworks based on tacit knowledge embedded in stakeholders.

The developed methodological framework aims to include societal groups in the climate policy-making process by acknowledging stakeholders’ role in developing transition pathways. The system map codifies stakeholder input in a structured and transparent manner.

This is the first study that clearly defines the system mapping approach in the frame of climate policy and introduces the first dedicated software option for researchers and decision makers to use for implementing this methodology.

This paper aims to provide a better understanding of the challenges and potential solutions in Visual Simultaneous Localization and Mapping (SLAM), laying the foundation for its applications in autonomous navigation, intelligent driving and other related domains.

In analyzing the latest research, the review presents representative achievements, including methods to enhance efficiency, robustness and accuracy. Additionally, the review provides insights into the future development direction of Visual SLAM, emphasizing the importance of improving system robustness when dealing with dynamic environments. The research methodology of this review involves a literature review and data set analysis, enabling a comprehensive understanding of the current status and prospects in the field of Visual SLAM.

This review aims to comprehensively evaluate the latest advances and challenges in the field of Visual SLAM. By collecting and analyzing relevant research papers and classic data sets, it reveals the current issues faced by Visual SLAM in complex environments and proposes potential solutions. The review begins by introducing the fundamental principles and application areas of Visual SLAM, followed by an in-depth discussion of the challenges encountered when dealing with dynamic objects and complex environments. To enhance the performance of SLAM algorithms, researchers have made progress by integrating different sensor modalities, improving feature extraction and incorporating deep learning techniques, driving advancements in the field.

To the best of the authors’ knowledge, the originality of this review lies in its in-depth analysis of current research hotspots and predictions for future development, providing valuable references for researchers in this field.

This paper aims to offer a novel participatory visual research method, the mapping of identity (MOI) protocol that embraces the complex nature of contemporary consumers’ lived reality.

The MOI protocol is a two-phase methodology. The first phase includes collage creation, based on a taxonomy of attachments, followed by an elicitation interview structured around the participant’s collage. In phase two, the categories elicited in phase one are synthesised into key themes in collaboration between the researcher and the participant.

MOI methodology provides an effective platform for participants to bring together disjointed memories, fragments and thoughts. Two individual cases are presented that seem similar on the surface; however, when deconstructing these narratives, their lived experiences and the effect that these narratives have on the construction of the self are very different. Treating participants as co-researchers and letting the choices they make in their collage creation lead the interview empowers the participant and enables the researcher to better understand their complex identity articulations.

This study contributes a visual methodology capable of exploring and celebrating the complexities of self-identity.

MOI is a useful tool for facilitating self-exploration in liquid markets. Marketing experts should provide materials that are not too confining and facilitate consumers in expressing multiple voices.

The participatory nature of MOI methodology allows for the emergence of stories from those that might otherwise go unheard, helping to understand unfamiliar and sometimes unrecognised identities.

Marketing literature recognises the complex nature of contemporary lived reality; however, some of the intricate aspects of this reality have not been dealt with in all their complexity. A reason for this gap is the paucity of suitable research methods. The MOI protocol presented in this paper addresses this, providing an effective visual tool to explore the complex web of contemporary consumer life.

The authors present a low-cost unmanned aerial vehicle (UAV) for autonomous flight and navigation in GPS-denied environments using an off-the-shelf smartphone as its core on-board processing unit. Thereby, the approach is independent from additional ground hardware and the UAV core unit can be easily replaced with more powerful hardware that simplifies setup updates as well as maintenance. The paper aims to discuss these issues.

The UAV is able to map, locate and navigate in an unknown indoor environment fusing vision-based tracking with inertial and attitude measurements. The authors choose an algorithmic approach for mapping and localization that does not require GPS coverage of the target area; therefore autonomous indoor navigation is made possible.

The authors demonstrate the UAVs capabilities of mapping, localization and navigation in an unknown 2D marker environment. The promising results enable future research on 3D self-localization and dense mapping using mobile hardware as the only on-board processing unit.

The proposed autonomous flight processing pipeline robustly tracks and maps planar markers that need to be distributed throughout the tracking volume.

Due to the cost-effective platform and the flexibility of the software architecture, the approach can play an important role in areas with poor infrastructure (e.g. developing countries) to autonomously perform tasks for search and rescue, inspection and measurements.

The authors provide a low-cost off-the-shelf flight platform that only requires a commercially available mobile device as core processing unit for autonomous flight in GPS-denied areas.

Purpose – This chapter focuses on how teacher candidates engage in a process of body mapping to narratively inquire into how their daily informal and formal music experiences inform elementary music teaching practices.

Methodology and findings – In a primary/junior music education course at Brock University, teacher candidates utilize a course assignment to create a visual narrative (body map), along with oral and written narratives that outline their music experiences. Through this narrative inquiry, teacher candidates become aware of how their personal lived experiences influence their perceptions about elementary music teaching. This chapter offers conceptualizations of five threads that emerged from the narratives: process of body mapping and musical experience, music everywhere, school influences, family, and fear.

Value – This inquiry deepens understandings of curriculum making possibilities in elementary music teacher education as teacher candidates begin to form their music teacher identity based on their lived experiences. Such visual, oral, and written narratives contribute to increased narrative understandings by demonstrating the power teacher candidates' personal music experiences have in shaping teacher identity and, in turn, teaching practice.

This paper aims to indicate that library guides seem to be unfamiliar to most students or not easy to find or use. Some improvements have been made by embedding the guides in the learning management system or promoting the guides in formal library instructional classes. Are there other ways to promote or improve the use of library guides? The author proposes an exploratory visual solution to minimize this gap between library users and library guides.

Guided by the cognitive load theory, the proposed solution is a knowledge map created with Freeplane. The proposal is illustrated by comparing a sample knowledge map with its content source, a subject guide in LibGuides, via three browsing paths in locating a recommended database for a particular course on the sample subject guide website.

The knowledge map can display contents in different ways and provide a simple and visual layout with direct access to the library resources, which may help lessen users’ intrinsic cognitive load, minimize extraneous load or promote germane load. The map can also be beneficial to librarians for preparing teaching materials or guides management.

The proposed solution can be implemented with Freeplane based on existing library guides or created from scratch.

The proposed solution addresses a gap in the library field, where the use of knowledge maps for library services is overlooked.

The purpose of this article is to explore the potential of visualization for corporate knowledge management.

The employed methodology consists of a taxonomy of visualization formats that are embedded in a conceptual framework to guide the application of visualization in knowledge management according to the type of knowledge that is visualized, the knowledge management objective, the target group, and the application situation. This conceptual framework is illustrated through real‐life examples.

The findings show that there is much room for knowledge management applications based on visualization beyond the mere referencing of experts or documents through knowledge maps.

The research implications thus consist of experimenting actively with new forms of visual knowledge representation and evaluating their benefits or potential drawbacks rigorously.

The authors encourage managers to look beyond simple diagrammatic representations of knowledge and explore alternative visual languages, such as visual metaphors or graphic narratives.

This paper consists of two elements: first, the systematic, descriptive and prescriptive approach towards visualization in knowledge management, and second the innovative examples of how to harness the power of visualization in knowledge management.

This paper aims to present a mosaicking method for underwater robotic applications, whose result can be provided to other perceptual systems for scene understanding such as real-time object recognition.

This method is called robust and large-scale mosaicking (ROLAMOS) and presents an efficient frame-to-frame motion estimation with outlier removal and consistency checking that maps large visual areas in high resolution. The visual mosaic of the sea-floor is created on-the-fly by a robust registration procedure that composes monocular observations and manages the computational resources. Moreover, the registration process of ROLAMOS aligns the observation to the existing mosaic.

A comprehensive set of experiments compares the performance of ROLAMOS to other similar approaches, using both data sets (publicly available) and live data obtained by a ROV operating in real scenes. The results demonstrate that ROLAMOS is adequate for mapping of sea-floor scenarios as it provides accurate information from the seabed, which is of extreme importance for autonomous robots surveying the environment that does not rely on specialized computers.

The ROLAMOS is suitable for robotic applications that require an online, robust and effective technique to reconstruct the underwater environment from only visual information.

This paper aims to evaluate four different simultaneous localization and mapping (SLAM) systems in the context of localization of multi-legged walking robots equipped with compact RGB-D sensors. This paper identifies problems related to in-motion data acquisition in a legged robot and evaluates the particular building blocks and concepts applied in contemporary SLAM systems against these problems. The SLAM systems are evaluated on two independent experimental set-ups, applying a well-established methodology and performance metrics.

Four feature-based SLAM architectures are evaluated with respect to their suitability for localization of multi-legged walking robots. The evaluation methodology is based on the computation of the absolute trajectory error (ATE) and relative pose error (RPE), which are performance metrics well-established in the robotics community. Four sequences of RGB-D frames acquired in two independent experiments using two different six-legged walking robots are used in the evaluation process.

The experiments revealed that the predominant problem characteristics of the legged robots as platforms for SLAM are the abrupt and unpredictable sensor motions, as well as oscillations and vibrations, which corrupt the images captured in-motion. The tested adaptive gait allowed the evaluated SLAM systems to reconstruct proper trajectories. The bundle adjustment-based SLAM systems produced best results, thanks to the use of a map, which enables to establish a large number of constraints for the estimated trajectory.

The evaluation was performed using indoor mockups of terrain. Experiments in more natural and challenging environments are envisioned as part of future research.

The lack of accurate self-localization methods is considered as one of the most important limitations of walking robots. Thus, the evaluation of the state-of-the-art SLAM methods on legged platforms may be useful for all researchers working on walking robots’ autonomy and their use in various applications, such as search, security, agriculture and mining.

The main contribution lies in the integration of the state-of-the-art SLAM methods on walking robots and their thorough experimental evaluation using a well-established methodology. Moreover, a SLAM system designed especially for RGB-D sensors and real-world applications is presented in details.