Single-aperture non-repetitive scanning LiDAR with Ethernet and multi-return output
Perception & Sensing · Non-repetitive scanning 3D LiDAR
Mid-70
A compact circular-FOV LiDAR with range explicitly separated by reflectivity under 100 klx ambient illumination.
7 known suppliers in this product family.
Mechanical or non-repetitive/solid-state 3D LiDAR product lines suitable for mobile robots, mapping or industrial perception. The list is bounded to the current RCI manufacturer-directory universe and separates exact-model audited suppliers from confirmed product-line suppliers.
EXACT-MODEL AUDITED
5 suppliers
Livox · Ouster · Hesai Technology · RoboSense · SICK
PRODUCT-LINE CONFIRMED
2 suppliers
Seyond · Blickfeld
This is a bounded discovery set, not a claim to enumerate every company worldwide. Open the full supplier landscape and official routes →
What this record can support today.
Coverage is computed from published, non-unknown fields. It measures the record—not product quality—and makes missing procurement evidence explicit.
Engineering field coverage82%
9 of 11 selected fields published
- Official product sourcepublished
- Lifecycle statepublished
- Mechanical dimensions / mass / mountingpublished
- Environmental operating limitsmissing
- Operating / detection rangepublished
- Resolution / sample ratepublished
- Field of viewpublished
- Accuracy / error definitionpublished
- Data interfacemissing
- Timing / synchronizationpublished
- Power input / consumptionpublished
Procurement field coverage15%
2 of 13 selected fields published
- Official product / sales routepublished
- Lifecycle statepublished
- Exact orderable SKU / part numbermissing
- Price with region, currency and quantitymissing
- Lead time with check datemissing
- Commercial availability / stock statemissing
- Minimum order quantitymissing
- Authorized distributor / sales channelmissing
- CAD / STEP / interface drawingmissing
- Certification with identifiermissing
- MTBF / failure / reliability datamissing
- Warranty termsmissing
- PCN / EOL / replacement noticemissing
Coverage measures whether a selected public field is present with non-U evidence. It is not a product score, compatibility result, certification, availability confirmation or procurement recommendation. Download the complete readiness coverage JSON →
11 bounded physical-evidence records.
Publisher independence, identity precision and measurement scope vary by row. Results remain bounded by the disclosed specimen, setup, workload and metric; an inconclusive mapping is evidence associated with the catalog model, not verification of a manufacturer claim.
| Measured result | Conditions | Claim mapping | Source |
|---|---|---|---|
| Independent point-cloud registration resultindependent_registration_error | 0.018 m static; 0.043 m dynamicProposed registration method; 35 static and 357 moving-platform scans; 0.2 s integration; sensor warmed for more than 30 minutes; returns below 1 m discarded; room dimensions physically referenced. | supportedThe catalog claim already carries a qualified external V source with its original condition and location. | Non-Repetitive Scanning LiDAR Sensor for Robust 3D Point Cloud Registration in Localization and Mapping Applications ↗Sections 5–6 and Table 2: static and dynamic registration experiments Checked 2026-08-10 |
| Independent distance-analysis dispersiondistance_analysis | 2.08 cm standard deviationFixed planar target measured from 1 m to 10 m against physically measured reference distances. | inconclusiveThe 1–10 m planar-target dispersion does not reproduce the manufacturer’s 20 m, 30% reflectivity and 25°C precision condition, so the ≤2 cm claim remains unresolved. | Non-Repetitive Scanning LiDAR Sensor for Robust 3D Point Cloud Registration in Localization and Mapping Applications ↗Section 3.8 and Figure 9 Checked 2026-08-24 |
| Observed non-repetitive scanning patternscanning_pattern | Rosette-form pattern observedPlanar scans examined at 100 ms, 200 ms and 400 ms integration times and multiple target distances. | inconclusiveObservation of a rosette-form scan pattern does not measure the 70.4° circular field boundary, so it is relevant context but not a field-of-view verification. | Non-Repetitive Scanning LiDAR Sensor for Robust 3D Point Cloud Registration in Localization and Mapping Applications ↗Section 3.9 and Figure 10 Checked 2026-08-24 |
| Static dataset registration timestatic_processing_time | 90 sProposed registration method; 35 scans; 0.2 s integration; Intel Core i9-10885H laptop, Windows 10 and MATLAB. | inconclusiveProcessing time describes the laptop, MATLAB implementation and scan count; by itself it does not resolve the published static or dynamic registration-error values. | Non-Repetitive Scanning LiDAR Sensor for Robust 3D Point Cloud Registration in Localization and Mapping Applications ↗Sections 5.1 and 6.2, Table 3 Checked 2026-08-24 |
| Moving-platform dataset registration timedynamic_processing_time | 900 sProposed registration method; 357 scans at about 1 m/s; 0.2 s integration; same laptop and software boundary. | inconclusiveThe 900 s runtime is an implementation-cost observation and does not independently confirm the dimensional registration-error result. | Non-Repetitive Scanning LiDAR Sensor for Robust 3D Point Cloud Registration in Localization and Mapping Applications ↗Sections 5.2 and 6.2, Table 3 Checked 2026-08-24 |
| Moving-platform localization RMSElocalization_rmse | Δx 0.026 m; Δy 0.021 m; θx 4.1°; θy 2.7°Proposed registration trajectory compared with the predefined mobile-robot trajectory before CFDT and loop-closure improvements. | inconclusiveTrajectory localization RMSE and angular error are different outputs from average dimensional registration error, despite coming from the same moving-platform experiment. | Non-Repetitive Scanning LiDAR Sensor for Robust 3D Point Cloud Registration in Localization and Mapping Applications ↗Section 6.3, Table 5 Checked 2026-08-24 |
| Improved moving-platform localization RMSEimproved_localization_rmse | Δx 0.017 m; Δy 0.011 m; θx 3.2°; θy 1.6°Same moving-platform dataset after curve-fitting derivative filtering and loop-closure constraints. | inconclusivePost-processing localization RMSE is not the same metric as the catalogued dimensional registration error and cannot replace it. | Non-Repetitive Scanning LiDAR Sensor for Robust 3D Point Cloud Registration in Localization and Mapping Applications ↗Section 6.3, Table 6 Checked 2026-08-24 |
| Improved moving-platform registration errorimproved_registration_error | 0.029 m, improved 32.6% from 0.043 mAverage absolute dimensional difference in Exp_2 after CFDT and loop closure. | supportedThe table explicitly reports the 0.043 m baseline used in the existing dynamic registration claim before improving it to 0.029 m, directly supporting that bounded baseline value. | Non-Repetitive Scanning LiDAR Sensor for Robust 3D Point Cloud Registration in Localization and Mapping Applications ↗Section 6.3, Table 7 Checked 2026-08-24 |
| Observed minimum and far-object range boundaryobserved_range_boundary | 0.05 m minimum; more than 200 m maximumTripod-mounted sensor tested indoors and outdoors; target reflectivity and 100 klx illumination were not controlled to the manufacturer claim conditions. | inconclusiveDetection beyond 200 m is relevant independent range evidence, but the paper does not control target reflectivity or 100 klx ambient illumination and therefore cannot resolve the three reflectivity-qualified endpoints. | Non-Repetitive Scanning LiDAR Sensor for Robust 3D Point Cloud Registration in Localization and Mapping Applications ↗Section 3.2 and Figures 2–3 Checked 2026-08-24 |
| Observed minimum rangeobserved_minimum_range | 0.05 mTripod-mounted close-object test; the paper notes degraded accuracy from 0.05 m to 0.25 m because of sensor filtering. | supportedThe paper directly reports the closest detected object at 0.05 m on the exact Mid-70 and separately discloses degraded accuracy through 0.25 m, supporting the bounded minimum-range value without implying close-range precision. | Non-Repetitive Scanning LiDAR Sensor for Robust 3D Point Cloud Registration in Localization and Mapping Applications ↗Section 3.2 and Figure 2 Checked 2026-08-24 |
| Observed valid-point streamobserved_valid_point_rate | About 100,000 valid points/sPaper characterization after an approximately 8 s startup delay; return mode was not stated and the 200,000 points/s dual-return mode was not tested separately. | inconclusiveThe observed stream of about 100,000 valid points/s is consistent with the single-return half of the claim, but the return mode is not stated and the 200,000 points/s dual-return value is not independently tested. | Non-Repetitive Scanning LiDAR Sensor for Robust 3D Point Cloud Registration in Localization and Mapping Applications ↗Section 3.1 Checked 2026-08-24 |
Reported values, conditions attached.
Evidence state describes the claim—not the prestige of the company or document.
| Claim | Reported value | Condition / boundary | State | Source |
|---|---|---|---|---|
| Detection range at 100 klxrange | 90 m @ 10%; 130 m @ 20%; 260 m @ 80% | Target reflectivity under 100 klx ambient illumination. | M · Manufacturer claimed | Livox Mid-70 specifications ↗Detection range, point rate, FOV, precision, timing and mechanical specifications Checked 2026-08-10 |
| Minimum rangemin_range | 0.05 m | Manufacturer specification. | M · Manufacturer claimed | Livox Mid-70 specifications ↗Detection range, point rate, FOV, precision, timing and mechanical specifications Checked 2026-08-10 |
| Point ratepoint_rate | 100,000 single; 200,000 dual points/s | Return mode changes rate. | M · Manufacturer claimed | Livox Mid-70 specifications ↗Detection range, point rate, FOV, precision, timing and mechanical specifications Checked 2026-08-10 |
| Field of viewfov | 70.4 circular ° | Circular FOV. | M · Manufacturer claimed | Livox Mid-70 specifications ↗Detection range, point rate, FOV, precision, timing and mechanical specifications Checked 2026-08-10 |
| Range precisionprecision | ≤2 cm | At 20 m, 30% reflectivity and 25°C. | M · Manufacturer claimed | Livox Mid-70 specifications ↗Detection range, point rate, FOV, precision, timing and mechanical specifications Checked 2026-08-10 |
| Synchronizationsync | IEEE 1588-2008 PTPv2, PPS and GPS | No additional condition stated in the selected source. | M · Manufacturer claimed | Livox Mid-70 specifications ↗Detection range, point rate, FOV, precision, timing and mechanical specifications Checked 2026-08-10 |
| Average powerpower | 8 W | Manufacturer average-power value. | M · Manufacturer claimed | Livox Mid-70 specifications ↗Detection range, point rate, FOV, precision, timing and mechanical specifications Checked 2026-08-10 |
| Weightmass | 580 g | No additional condition stated in the selected source. | M · Manufacturer claimed | Livox Mid-70 specifications ↗Detection range, point rate, FOV, precision, timing and mechanical specifications Checked 2026-08-10 |
| Independent point-cloud registration resultindependent_registration_error | 0.018 m static; 0.043 m dynamic | Proposed registration method; 35 static and 357 moving-platform scans; 0.2 s integration; sensor warmed for more than 30 minutes; returns below 1 m discarded; room dimensions physically referenced.These are sensor-plus-algorithm mapping results in two disclosed scenes, not an independent validation of the intrinsic ≤2 cm range-precision claim or maximum detection range. | V · Independently verified | Non-Repetitive Scanning LiDAR Sensor for Robust 3D Point Cloud Registration in Localization and Mapping Applications ↗Sections 5–6 and Table 2: static and dynamic registration experiments Checked 2026-08-10 |
Known limits
What this record does not prove.
- Range depends strongly on reflectivity and ambient illumination.
- Point rate doubles only in dual-return mode.
- The 5 cm minimum range does not imply full precision at that distance.