When to Trust the BIM: Identifiability-Aware LiDAR-Inertial--BIM Fusion under As-Built Deviations
Overview
This research presents a method for robot localization that fuses LiDAR-inertial odometry with building information models (BIM) while accounting for as-built deviations.
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Event evidence and corrections
0 attributed source owners. Ownership does not establish independent confirmation. Quantities are reported separately and are never added together.
Reported quantity · conditions: 0 other · Basis not reported · Differing source assertions
“bution 5,574 times, with no incorrect attributions. In paired fault injections in a laboratory room with its IFC model and on three floors of the SLABIM dataset, it raised no off-target attribution (0 of 168 conditions in the laboratory; 0 of 1,302 on SLABIM) and attributed all eight laboratory faults predicted to be attributable, whereas both reproduced baselines raised off-target structural flags in every setting.”
Exact source · revision 1Source owner not reported
Reported quantity · cases: 1302 other · Basis not reported · Differing source assertions
“bution 5,574 times, with no incorrect attributions. In paired fault injections in a laboratory room with its IFC model and on three floors of the SLABIM dataset, it raised no off-target attribution (0 of 168 conditions in the laboratory; 0 of 1,302 on SLABIM) and attributed all eight laboratory faults predicted to be attributable, whereas both reproduced baselines raised off-target structural flags in every setting.”
Exact source · revision 1Source owner not reported
Reported quantity · faults: 8 other · Basis not reported
“bution 5,574 times, with no incorrect attributions. In paired fault injections in a laboratory room with its IFC model and on three floors of the SLABIM dataset, it raised no off-target attribution (0 of 168 conditions in the laboratory; 0 of 1,302 on SLABIM) and attributed all eight laboratory faults predicted to be attributable, whereas both reproduced baselines raised off-target structural flags in every setting.”
Exact source · revision 1Source owner not reported
Reported quantity · conditions: 168 other · Basis not reported · Differing source assertions
“bution 5,574 times, with no incorrect attributions. In paired fault injections in a laboratory room with its IFC model and on three floors of the SLABIM dataset, it raised no off-target attribution (0 of 168 conditions in the laboratory; 0 of 1,302 on SLABIM) and attributed all eight laboratory faults predicted to be attributable, whereas both reproduced baselines raised off-target structural flags in every setting.”
Exact source · revision 1Source owner not reported
Reported quantity · cases: 5600 other · Basis not reported · Differing source assertions
“s BIM updates and BIM-based pose corrections in the ambiguous directions, and recovers once new references arrive. In the pre-registered two-dimensional study, on cases whose fault is detectable but not attributable, the method's conditional error rate was 0.83%, against 31.67% for a baseline with uncertainty-based abstention at the same answer rate. On the 5,600 attributable cases, the method reported a unique attri”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalWhen to Trust the BIM: Identifiability-Aware LiDAR-Inertial--BIM Fusion under As-Built Deviations
This research presents a method for robot localization that fuses LiDAR-inertial odometry with building information models (BIM) while accounting for as-built deviations.
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