Spatial mapping: Difference between revisions
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* '''Problematic Surfaces''': Onboard sensors often struggle with certain types of materials. Transparent surfaces like glass, highly reflective surfaces like mirrors, and textureless or dark, light-absorbing surfaces can fail to return usable data to depth sensors, resulting in gaps or inaccuracies in the map.<ref name="UnityDocs"/><ref name="HoloLensSpaces"/><ref name="MagicLeapMappingDocs">{{cite web |url=https://developer-docs.magicleap.cloud/docs/guides/features/spatial-mapping/ |title=Real-time World Sensing |publisher=Magic Leap |access-date=2025-10-23}}</ref> | * '''Problematic Surfaces''': Onboard sensors often struggle with certain types of materials. Transparent surfaces like glass, highly reflective surfaces like mirrors, and textureless or dark, light-absorbing surfaces can fail to return usable data to depth sensors, resulting in gaps or inaccuracies in the map.<ref name="UnityDocs"/><ref name="HoloLensSpaces"/><ref name="MagicLeapMappingDocs">{{cite web |url=https://developer-docs.magicleap.cloud/docs/guides/features/spatial-mapping/ |title=Real-time World Sensing |publisher=Magic Leap |access-date=2025-10-23}}</ref> | ||
* '''Drift''': Tracking systems that rely on [[odometry]] (estimating motion from sensor data) are susceptible to small, accumulating errors over time. This phenomenon, known as '''drift''', can cause the digital map to become misaligned with the real world. While algorithms use techniques like [[loop closure]] to correct for drift, it can still be a significant problem in large, feature-poor environments (like a long, white hallway).<ref name="MilvusSLAM"/><ref name="SLAMSystems"/> | * '''[[Drift]]''': Tracking systems that rely on [[odometry]] (estimating motion from sensor data) are susceptible to small, accumulating errors over time. This phenomenon, known as '''drift''', can cause the digital map to become misaligned with the real world. While algorithms use techniques like [[loop closure]] to correct for drift, it can still be a significant problem in large, feature-poor environments (like a long, white hallway).<ref name="MilvusSLAM"/><ref name="SLAMSystems"/> | ||
* '''Scale and Boundaries''': The way spatial data is aggregated and defined can influence analytical results, a concept known in geography as the [[Modifiable Areal Unit Problem]] (MAUP). This problem highlights that statistical outcomes can change based on the shape and scale of the zones used for analysis, which has parallels in how room-scale maps are chunked and interpreted.<ref name="MAUP1">{{cite web |url=https://pmc.ncbi.nlm.nih.gov/articles/PMC7254930/ |title=The modifiable areal unit problem in ecological community data |publisher=PLOS ONE |access-date=2025-10-23}}</ref><ref name="MAUP2">{{cite web |url=https://zenn-wong.medium.com/the-challenges-of-using-maps-in-policy-making-510e3fcb8eb3 |title=The Challenges of Using Maps in Policy-Making |publisher=Medium |access-date=2025-10-23}}</ref> | * '''Scale and Boundaries''': The way spatial data is aggregated and defined can influence analytical results, a concept known in geography as the [[Modifiable Areal Unit Problem]] (MAUP). This problem highlights that statistical outcomes can change based on the shape and scale of the zones used for analysis, which has parallels in how room-scale maps are chunked and interpreted.<ref name="MAUP1">{{cite web |url=https://pmc.ncbi.nlm.nih.gov/articles/PMC7254930/ |title=The modifiable areal unit problem in ecological community data |publisher=PLOS ONE |access-date=2025-10-23}}</ref><ref name="MAUP2">{{cite web |url=https://zenn-wong.medium.com/the-challenges-of-using-maps-in-policy-making-510e3fcb8eb3 |title=The Challenges of Using Maps in Policy-Making |publisher=Medium |access-date=2025-10-23}}</ref> | ||