RAIS: Towards A Robotic Mapping and Assessment Tool for Indoor Accessibility Using Commodity Hardware

This paper presents a comparative analysis of SLAM Toolbox and Cartographer mapping performance in both simulated and real-world environments using ROS 2. The aim of the study is to evaluate the effectiveness, accuracy, and resource utilization of each Simultaneous Localization and Mapping (SLAM) tool under identical conditions. The experiments were conducted using the Humble Hawksbill distribution of ROS 2, with mapping tasks performed in indoor environments via Gazebo simulation and physical robot tests. Results show that SLAM Toolbox demonstrated slightly more consistent map generation in environments that included human movement and small object relocations. It achieved an Absolute Trajectory Error (ATE) of 0.13 m, compared to 0.21 m for Cartographer under identical test conditions. However, Toolbox required approximately 70% CPU usage, 293 MB RAM, and a startup time of 5.2 s, reflecting higher computational demand and configuration complexity. In contrast, Cartographer exhibited slower map generation and slightly higher RAM usage (299 MB) in simulation, while requiring higher CPU load (80%) and showing greater sensitivity to parameter tuning, which contributed to less accurate localization in noise-free simulations. This study highlights the advantages and limitations of both SLAM technologies and provides practical guidance for selecting appropriate SLAM solutions in robotic mapping and autonomous navigation tasks, particularly for systems deployed on resource-constrained platforms.

MotivationNanopore sequencers allow targeted sequencing of interesting nucleotide sequences by rejecting other sequences from individual pores. This feature facilitates the enrichment of low-abundant sequences by depleting overrepresented ones in-silico. Existing tools for adaptive sampling either apply signal alignment, which cannot handle human-sized reference sequences, or apply read mapping in sequence space relying on fast graphical processing units (GPU) base callers for real-time read rejection. Using nanopore long-read mapping tools is also not optimal when mapping shorter reads as usually analyzed in adaptive sampling applications.ResultsHere, we present a new approach for nanopore adaptive sampling that combines fast CPU and GPU base calling with read classification based on Interleaved Bloom Filters. ReadBouncer improves the potential enrichment of low abundance sequences by its high read classification sensitivity and specificity, outperforming existing tools in the field. It robustly removes even reads belonging to large reference sequences while running on commodity hardware without GPUs, making adaptive sampling accessible for in-field researchers. Readbouncer also provides a user-friendly interface and installer files for end-users without a bioinformatics background.Availability and implementationThe C++ source code is available at https://gitlab.com/dacs-hpi/readbouncer.Supplementary informationSupplementary data are available at Bioinformatics online.

SUMMARYAn autonomous robot must obtain information about its surroundings to accomplish multiple tasks that are greatly improved when this information is efficiently incorporated into a map. Some examples are navigation, manipulation, localization, etc. This mapping problem has been an important research area in mobile robotics during last decades. It does not have a unique solution and can be divided into multiple sub-problems. Two different aspects of the mobile robot mapping problem are addressed in this work. First, we have developed a Differential Evolution-based scan matching algorithm that operates with high accuracy in three-dimensional environments. The map obtained by an autonomous robot must be consistent after registration. It is basic to detect when the robot is navigating around a previously visited place in order to minimize the accumulated error. This phase, which is called loop detection, is the second aspect studied here. We have developed an algorithm that extracts the most important features from two different three-dimensional laser scans in order to obtain a loop indicator that is used to detect when the robot is visiting a known place. This approach allows the introduction of very different characteristics in the descriptor. First, the surface features include the geometric forms of the scan (lines, planes, and spheres). Second, the numerical features are values that describe several numerical properties of the measurements: volume, average range, curvature, etc. Both algorithms have been tested with real data to demonstrate that these are efficient tools to be used in mapping tasks.

In Durban, South Africa, the apartheid planning legacy led to the creation of various development nodes within the city. Nevertheless, such nodes are most often developer-driven and do not cater to the needs of existing communities. The resultant effect is the lost and insurgent space created between urban nodes within the city. Insurgency can be seen in the form of informal settlements, self-help housing and urban homesteaders (squatters and homeless, using private property or government land). Sociocultural segregation, urban despair, insurgency and lost space now exist between these nodes. In response to these challenges, adaptive reuse of buildings and urban space represents a sustainable solution by extending buildings’ life and reducing the overall carbon footprint. Low-carbon emissions are a key component of sustainable urban environments and in turn contribute to mitigate the consequences of climate change. Such approach forms part of sustainable urban regeneration, with social and economic benefits to society. To develop sustainable urban neighbourhoods all stakeholders and inhabitants need to be meaning fully involved, towards more resilient cities. This paper applied a set of hybrid mapping tools—drone mapping, transect walks, sketches and observations—to capture lost and insurgent space. This helped defining lost and abandoned space within the study area, to understand how it is currently being used and evaluate its potential for adaptive reuse. By mapping spaces between development nodes in three case studies in Durban inner city, this paper seeks to analyse characteristics and factors that should be considered to determine the adaptive reuse potential of buildings, towards developing more inclusive and resilient cities.

The need for reliable maps of subterranean spaces too hazardous for humans to occupy has motivated the development of robotic mapping tools suited to these domains. As such, this work describes a system developed for autonomous topological exploration of mine environments to facilitate the process of mapping. The exploration framework is based upon the interaction of three main components: Node detection, node matching, and edge exploration. Node detection robustly identifies mine corridor intersections from sensor data and uses these features as the building blocks of a topological map. Node matching compares newly observed intersections to those stored in the map, providing global localization during exploration. Edge exploration translates topological exploration objectives into locomotion along mine corridors. This article describes both the robotic platform and the algorithms developed for exploration, and presents results from experiments conducted at a research coal mine near Pittsburgh, PA. © 2006 Wiley Periodicals, Inc.

The need for reliable maps of subterranean spaces too hazardous for humans to occupy has motivated the development of robotic mapping tools. For such systems to be fully autonomous, they must be able to deal with all varieties of subterranean environments, including those containing loops. This paper presents an approach for an autonomous mobile robot to determine if the area currently being explored has been previously visited. Combined with other techniques in topological mapping, this approach will allow for the fully autonomous general exploration of subterranean spaces. Data collected from a research coal mine is used to experimentally verify our approach.