Ve an os:StructuralModel, linked together with the relation os:hasModel. kn:MathematicalThing, that denotes all mathematical concepts made use of throughout the formalization with the know-how obtained when solving the SLAM challenge. Examples of this class are vectors and matrices. os:FeatureThing, which represents the traits that a physical issue can have; one example is, color or shape. isro:TemporalThing, which represents all the entities necessary to model the time related using the events that happen through the SLAM course of action. Its primary subclasses are: isro:ML-SA1 web TimePoint and isro:TimeInterval. os:PositionalThing, utilized for concepts connected towards the positioning of both robots and objects within the working environment.Figure two. Key concepts and relationships of OntoSLAM related to Positioning.Figure three. OntoSLAM main classes of Robot Information and facts and Icosabutate supplier Environment Mapping.Figure two moreover shows classes of OntoSLAM connected to positioning. To represent dynamic positions and uncertainty, class os:Position is associated to isro:TimePoint class, by way of the relation fr:PosAtTime, and towards the probability (os:Probability) of getting in that position, by way of the relation os:hasProbability. Furthermore, the os:Mobile class is made use of to model mobile objects and also the os:Reconfigurable class is made use of to model objects that could alter their pose but not their position. Figure 3 shows the principle classes that model Robot Info and Environment Mapping. On the list of primary aspects is the class hierarchy to model the parts. The os:Compo-Robotics 2021, ten,eight ofsedPart class represents the set of several os:AtomicPart, which might be the os:BasePart, that determines the position with the robot, or os:RegularPart, which can be os:Actuator or os:Sensor variety. Also, an os:Portion has linked visual characteristics, such as shape (os:Shape) that also has a worth of uncertainty (os:Probability), which may be updated as the robot performs the SLAM. This os:Shape could be a known geometric figure, for example os:Cylinder, os:Plane, os:Sphere, os:Box. Even so, in case it truly is not specific to which figure it belongs, it might be modeled as os:Undefined, a class specialized in two types: os:HeightMap and os:OcuppancyGrid, that are two formats utilised in robotics to save maps devoid of losing information and facts. Other options that will be modeled are colors (os:Colour) and the dimensions (os:Dimension) in the visual element with the os:Element. These last two attributes and also the os:Shape are subclasses of os:AbstractThing. Figure 4 shows the primary classes that model Temporal Information. For this module the ISRO ontology has been taken as a base, beginning from its base concept isro:TemporalThing, which in turns is specialized in two subclasses: isro:TimePoint and isro:TimeInterval. The initial one particular is linked with the position (os:Position) attributed to every os:Component, through the relation os:atTime. With this notion it is actually doable to model the trajectory on the robot over the time. On the other hand, with all the isro:TimeInterval class, it is feasible to model processes which have a specific duration. For example, the time in which the SLAM procedure was performed. To determine this duration, the subclasses isro:StartInterval and isro:EndInterval are made use of. Additionally, the class os:State, refers to no matter if the object was moved or not in the time becoming evaluated, together with the following four values: Reconfigured, Moved, Not reconfigured, or Not moved. These values are set through the os:isMobile and os:isReconfigurable relationships.Figur.