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Paper Citation Record · LEDGER

LXD-SLAM: LiDAR+X Dense SLAM with $\sum_{i=0}^{5}C_5^i$ Configurable Sensor Combinations

As of 11 August 2026, this Paper Citation Record lists 53 of 53 outbound references and 0 inbound Pith citation observations for arXiv:2606.27811.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2606.27811 v1

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measured 53 of 53 reference resolution

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measured 53 of 53 standing notices

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53 of 53 outbound references displayed

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Outbound references

Observation 30c8853c-c650-45a9-8ba8-d87667d36322 · outbound

This paper cites Present and future of SLAM in extreme environments: The DARPA SubT challenge,.

LXD-SLAM: LiDAR+X Dense SLAM with $\sum_{i=0}^{5}C_5^i$ Configurable Sensor Combinations Present and future of SLAM in extreme environments: The DARPA SubT challenge,

Reference 1

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Observation 00e923c3-dfaa-43a2-91c0-d96c869abf46 · outbound

This paper cites Tightly coupled 3D lidar inertial odometry and mapping,.

LXD-SLAM: LiDAR+X Dense SLAM with $\sum_{i=0}^{5}C_5^i$ Configurable Sensor Combinations Tightly coupled 3D lidar inertial odometry and mapping,

Reference 2

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Observation 9adccddd-9b60-4f2d-ae13-249d237d70f5 · outbound

This paper cites LIO-SAM: Tightly-coupled lidar inertial odometry via smoothing and mapping,.

LXD-SLAM: LiDAR+X Dense SLAM with $\sum_{i=0}^{5}C_5^i$ Configurable Sensor Combinations LIO-SAM: Tightly-coupled lidar inertial odometry via smoothing and mapping,

Reference 3

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Observation e1a2b8a9-39d2-401f-995c-bcdbee39cc5f · outbound

This paper cites LIO-EKF: High frequency lidar-inertial odometry using extended kalman filters,.

LXD-SLAM: LiDAR+X Dense SLAM with $\sum_{i=0}^{5}C_5^i$ Configurable Sensor Combinations LIO-EKF: High frequency lidar-inertial odometry using extended kalman filters,

Reference 4

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Observation c1291aee-41fb-4405-aa74-c9cf6e324349 · outbound

This paper cites LINS: A lidar-inertial state estimator for robust and efficient navigation,.

LXD-SLAM: LiDAR+X Dense SLAM with $\sum_{i=0}^{5}C_5^i$ Configurable Sensor Combinations LINS: A lidar-inertial state estimator for robust and efficient navigation,

Reference 5

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Observation 8bf44bc7-13f5-4ea7-9ac0-2b6d35fc044c · outbound

This paper cites FAST-LIO2: Fast direct lidar-inertial odometry,.

LXD-SLAM: LiDAR+X Dense SLAM with $\sum_{i=0}^{5}C_5^i$ Configurable Sensor Combinations FAST-LIO2: Fast direct lidar-inertial odometry,

Reference 6

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Observation f72b5d1c-3ff0-4d5c-9c14-7f86916764de · outbound

This paper cites Faster-LIO: Lightweight tightly coupled lidar-inertial odometry using parallel sparse incremental voxels,.

LXD-SLAM: LiDAR+X Dense SLAM with $\sum_{i=0}^{5}C_5^i$ Configurable Sensor Combinations Faster-LIO: Lightweight tightly coupled lidar-inertial odometry using parallel sparse incremental voxels,

Reference 7

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Observation 16eb5ed8-3984-46e7-880c-a9b014767989 · outbound

This paper cites Visual-lidar odometry and mapping: low-drift, robust, and fast,.

LXD-SLAM: LiDAR+X Dense SLAM with $\sum_{i=0}^{5}C_5^i$ Configurable Sensor Combinations Visual-lidar odometry and mapping: low-drift, robust, and fast,

Reference 8

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Observation a73914a9-27f2-4a8d-94ba-bbd1bd207fca · outbound

This paper cites LVI-SAM: Tightly-coupled lidar-visual-inertial odometry via smoothing and mapping,.

LXD-SLAM: LiDAR+X Dense SLAM with $\sum_{i=0}^{5}C_5^i$ Configurable Sensor Combinations LVI-SAM: Tightly-coupled lidar-visual-inertial odometry via smoothing and mapping,

Reference 9

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Observation a458201d-4636-4d10-800e-fc75db5cb67c · outbound

This paper cites LIC-Fusion: Lidar- inertial-camera odometry,.

LXD-SLAM: LiDAR+X Dense SLAM with $\sum_{i=0}^{5}C_5^i$ Configurable Sensor Combinations LIC-Fusion: Lidar- inertial-camera odometry,

Reference 10

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Observation 369f8f87-5e1e-48a3-a4ab-c41e86a36973 · outbound

This paper cites R 2LIVE: A robust, real-time, lidar-inertial-visual tightly-coupled state estimator and mapping,.

LXD-SLAM: LiDAR+X Dense SLAM with $\sum_{i=0}^{5}C_5^i$ Configurable Sensor Combinations R 2LIVE: A robust, real-time, lidar-inertial-visual tightly-coupled state estimator and mapping,

Reference 11

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Observation dbfdcc81-d875-4b51-8977-65930d069a65 · outbound

This paper cites R3LIVE: A robust, real-time, RGB-colored, LiDAR- inertial-visual tightly-coupled state estimation and mapping package,.

LXD-SLAM: LiDAR+X Dense SLAM with $\sum_{i=0}^{5}C_5^i$ Configurable Sensor Combinations R3LIVE: A robust, real-time, RGB-colored, LiDAR- inertial-visual tightly-coupled state estimation and mapping package,

Reference 12

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Observation 984f6f4a-b240-44eb-955a-dcd31151ee1b · outbound

This paper cites FAST-LIVO: Fast and tightly-coupled sparse-direct LiDAR-inertial-visual odometry,.

LXD-SLAM: LiDAR+X Dense SLAM with $\sum_{i=0}^{5}C_5^i$ Configurable Sensor Combinations FAST-LIVO: Fast and tightly-coupled sparse-direct LiDAR-inertial-visual odometry,

Reference 13

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Observation a11503c9-c42e-46e6-aff6-0d37a3dce7b8 · outbound

This paper cites FAST-LIVO2: Fast, direct lidar–inertial–visual odometry,.

LXD-SLAM: LiDAR+X Dense SLAM with $\sum_{i=0}^{5}C_5^i$ Configurable Sensor Combinations FAST-LIVO2: Fast, direct lidar–inertial–visual odometry,

Reference 14

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Observation e9359148-27d1-4d2e-903d-6498e479b601 · outbound

This paper cites Robust odometry and mapping for multi-lidar systems with online extrinsic calibration,.

LXD-SLAM: LiDAR+X Dense SLAM with $\sum_{i=0}^{5}C_5^i$ Configurable Sensor Combinations Robust odometry and mapping for multi-lidar systems with online extrinsic calibration,

Reference 15

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Observation d324f97e-647c-4c3b-bc28-62bd2019f5b5 · outbound

This paper cites LIWO: LiDAR-inertial-wheel odometry,.

LXD-SLAM: LiDAR+X Dense SLAM with $\sum_{i=0}^{5}C_5^i$ Configurable Sensor Combinations LIWO: LiDAR-inertial-wheel odometry,

Reference 16

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Observation 0fec9c36-0ccb-423e-ab2b-08180b3dd8c2 · outbound

This paper cites A General Optimization-based Framework for Global Pose Estimation with Multiple Sensors.

LXD-SLAM: LiDAR+X Dense SLAM with $\sum_{i=0}^{5}C_5^i$ Configurable Sensor Combinations A General Optimization-based Framework for Global Pose Estimation with Multiple Sensors

Reference 17

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source=pdf_text observed=2026-06-29T04:38:21.101435Z digest=sha256:e15780c6bd219857a7c18631b8698549f07863e41fc882c9019ec2a197ecc543

Observation f785dc76-0ec0-49d2-a7f9-9ff8a88e175c · outbound

This paper cites GVINS: Tightly coupled gnss–visual–inertial fusion for smooth and consistent state estimation,.

LXD-SLAM: LiDAR+X Dense SLAM with $\sum_{i=0}^{5}C_5^i$ Configurable Sensor Combinations GVINS: Tightly coupled gnss–visual–inertial fusion for smooth and consistent state estimation,

Reference 18

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Observation 34d15146-76bd-486a-8c48-6cf0b03c25e7 · outbound

This paper cites LOAM: Lidar odometry and mapping in real- time,.

LXD-SLAM: LiDAR+X Dense SLAM with $\sum_{i=0}^{5}C_5^i$ Configurable Sensor Combinations LOAM: Lidar odometry and mapping in real- time,

Reference 19

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Observation d4972b84-870e-45a0-af2d-8be04dbaffa4 · outbound

This paper cites LeGO-LOAM: Lightweight and ground-optimized lidar odometry and mapping on variable terrain,.

LXD-SLAM: LiDAR+X Dense SLAM with $\sum_{i=0}^{5}C_5^i$ Configurable Sensor Combinations LeGO-LOAM: Lightweight and ground-optimized lidar odometry and mapping on variable terrain,

Reference 20

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Observation 7ed45d2a-8d52-4e24-b8b5-e52b482972c2 · outbound

This paper cites F-LOAM : Fast lidar odometry and mapping,.

LXD-SLAM: LiDAR+X Dense SLAM with $\sum_{i=0}^{5}C_5^i$ Configurable Sensor Combinations F-LOAM : Fast lidar odometry and mapping,

Reference 21

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Observation 6ab14b0c-ec07-43a9-8cd3-4f446cf660b1 · outbound

This paper cites FAST-LIO: A fast, robust lidar-inertial odometry package by tightly-coupled iterated kalman filter,.

LXD-SLAM: LiDAR+X Dense SLAM with $\sum_{i=0}^{5}C_5^i$ Configurable Sensor Combinations FAST-LIO: A fast, robust lidar-inertial odometry package by tightly-coupled iterated kalman filter,

Reference 22

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Observation 6b69d973-210b-4f74-b25e-71dd8cec8e65 · outbound

This paper cites Point-LIO: Robust high-bandwidth light detection and ranging inertial odometry,.

LXD-SLAM: LiDAR+X Dense SLAM with $\sum_{i=0}^{5}C_5^i$ Configurable Sensor Combinations Point-LIO: Robust high-bandwidth light detection and ranging inertial odometry,

Reference 23

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Observation 249f85ad-0afc-4ece-8480-66976c6263a9 · outbound

This paper cites DEMO: Depth enhanced monocular odometry,.

LXD-SLAM: LiDAR+X Dense SLAM with $\sum_{i=0}^{5}C_5^i$ Configurable Sensor Combinations DEMO: Depth enhanced monocular odometry,

Reference 24

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Observation b1f2e64f-fd21-4c0a-8874-2d496c9a3d9e · outbound

This paper cites LIC-Fusion 2.0: Lidar-inertial-camera odometry with sliding-window plane-feature tracking,.

LXD-SLAM: LiDAR+X Dense SLAM with $\sum_{i=0}^{5}C_5^i$ Configurable Sensor Combinations LIC-Fusion 2.0: Lidar-inertial-camera odometry with sliding-window plane-feature tracking,

Reference 25

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Observation caf60558-951b-4a98-a099-b5168c33659a · outbound

This paper cites VINS-Mono: A robust and versatile monocular visual-inertial state estimator,.

LXD-SLAM: LiDAR+X Dense SLAM with $\sum_{i=0}^{5}C_5^i$ Configurable Sensor Combinations VINS-Mono: A robust and versatile monocular visual-inertial state estimator,

Reference 26

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Observation 23fb89e1-fe29-4e0f-b516-dbb5aed9702f · outbound

This paper cites Scalability in perception for autonomous driving: Waymo open dataset,.

LXD-SLAM: LiDAR+X Dense SLAM with $\sum_{i=0}^{5}C_5^i$ Configurable Sensor Combinations Scalability in perception for autonomous driving: Waymo open dataset,

Reference 27

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Observation 86242e6d-6282-440f-b5a8-7b96a7e385b4 · outbound

This paper cites nuscenes: A multimodal dataset for autonomous driving,.

LXD-SLAM: LiDAR+X Dense SLAM with $\sum_{i=0}^{5}C_5^i$ Configurable Sensor Combinations nuscenes: A multimodal dataset for autonomous driving,

Reference 28

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Observation 4b9024c6-4965-4036-b0fc-7a71d1452563 · outbound

This paper cites KITTI-360: A novel dataset and bench- marks for urban scene understanding in 2D and 3D,.

LXD-SLAM: LiDAR+X Dense SLAM with $\sum_{i=0}^{5}C_5^i$ Configurable Sensor Combinations KITTI-360: A novel dataset and bench- marks for urban scene understanding in 2D and 3D,

Reference 29

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Observation f25fcf19-50ff-474e-a2e0-69e38ce1c6d2 · outbound

This paper cites MLS-SLAM: Multi-level submap guided lidar SLAM,.

LXD-SLAM: LiDAR+X Dense SLAM with $\sum_{i=0}^{5}C_5^i$ Configurable Sensor Combinations MLS-SLAM: Multi-level submap guided lidar SLAM,

Reference 30

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Observation 517e6bc2-cebe-4d04-9540-600441ab77de · outbound

This paper cites MLIOM-AB: Multi-lidar-inertial-odometry and mapping for autonomous buses,.

LXD-SLAM: LiDAR+X Dense SLAM with $\sum_{i=0}^{5}C_5^i$ Configurable Sensor Combinations MLIOM-AB: Multi-lidar-inertial-odometry and mapping for autonomous buses,

Reference 31

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Observation 845eccbc-65f1-4ce9-880f-905d02731e2a · outbound

This paper cites Multi-LIO: A lightweight multiple lidar-inertial odometry system,.

LXD-SLAM: LiDAR+X Dense SLAM with $\sum_{i=0}^{5}C_5^i$ Configurable Sensor Combinations Multi-LIO: A lightweight multiple lidar-inertial odometry system,

Reference 32

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Observation 407fb234-53c1-4aae-a2f5-04d365234654 · outbound

This paper cites VINS on wheels,.

LXD-SLAM: LiDAR+X Dense SLAM with $\sum_{i=0}^{5}C_5^i$ Configurable Sensor Combinations VINS on wheels,

Reference 33

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Observation 83f098c0-54e6-4ef8-b68f-2da2581a0390 · outbound

This paper cites Efficient surfel-based SLAM using 3D laser range data in urban environments,.

LXD-SLAM: LiDAR+X Dense SLAM with $\sum_{i=0}^{5}C_5^i$ Configurable Sensor Combinations Efficient surfel-based SLAM using 3D laser range data in urban environments,

Reference 34

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Observation b4eacb04-b758-4351-9db3-950f290deb03 · outbound

This paper cites SuMa++: Efficient lidar-based semantic SLAM,.

LXD-SLAM: LiDAR+X Dense SLAM with $\sum_{i=0}^{5}C_5^i$ Configurable Sensor Combinations SuMa++: Efficient lidar-based semantic SLAM,

Reference 35

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source=pdf_text observed=2026-06-29T04:38:21.101435Z digest=sha256:7e382852b767e65348e172b9c6451680d9cfa9103e2392118e71ec6e8a6bea0c

Observation be3b35f0-8753-42e8-ab65-eaa298a133d9 · outbound

This paper cites Efficient and probabilistic adaptive voxel mapping for accurate online lidar odometry,.

LXD-SLAM: LiDAR+X Dense SLAM with $\sum_{i=0}^{5}C_5^i$ Configurable Sensor Combinations Efficient and probabilistic adaptive voxel mapping for accurate online lidar odometry,

Reference 36

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source=pdf_text observed=2026-06-29T04:38:21.101435Z digest=sha256:0745620ee3f374d0a9885ad0a20419099830c38de5164a2f2a5f17cb54e66a21

Observation fa7488b8-f5a2-4e27-8302-246840e735da · outbound

This paper cites BALM: Bundle adjustment for lidar mapping,.

LXD-SLAM: LiDAR+X Dense SLAM with $\sum_{i=0}^{5}C_5^i$ Configurable Sensor Combinations BALM: Bundle adjustment for lidar mapping,

Reference 37

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source=pdf_text observed=2026-06-29T04:38:21.101435Z digest=sha256:2360fb198d122e75c919e0c0e4eee9518aac213ada1fdd0326e63b7962cc0baf

Observation 1efb3bd3-dfaa-4fa9-8735-5dcb72bb0179 · outbound

This paper cites NICE-SLAM: Neural implicit scalable encoding for SLAM,.

LXD-SLAM: LiDAR+X Dense SLAM with $\sum_{i=0}^{5}C_5^i$ Configurable Sensor Combinations NICE-SLAM: Neural implicit scalable encoding for SLAM,

Reference 38

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Observation 0bea8848-b743-4a26-bffb-99ee799ac2cf · outbound

This paper cites Gaussian splatting SLAM,.

LXD-SLAM: LiDAR+X Dense SLAM with $\sum_{i=0}^{5}C_5^i$ Configurable Sensor Combinations Gaussian splatting SLAM,

Reference 39

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source=pdf_text observed=2026-06-29T04:38:21.101435Z digest=sha256:78e4ad083bea7396222cfc910e7114bb2d7a23e1911576cb9bc4881da80d6aa4

Observation af578fda-c134-476c-b7c2-96cd5ab97259 · outbound

This paper cites GP-SLAM+: real-time 3D lidar SLAM based on improved regionalized Gaussian process map reconstruction,.

LXD-SLAM: LiDAR+X Dense SLAM with $\sum_{i=0}^{5}C_5^i$ Configurable Sensor Combinations GP-SLAM+: real-time 3D lidar SLAM based on improved regionalized Gaussian process map reconstruction,

Reference 40

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source=pdf_text observed=2026-06-29T04:38:21.101435Z digest=sha256:652b25ffc1e61e140f1379e4d62d8ddc2e0fc918d10a75bc14a7b6773ec6dedd

Observation e5258a3d-5b5e-435a-ae89-4eab1a2dd5db · outbound

This paper cites SLAMesh: Real-time lidar simul- taneous localization and meshing,.

LXD-SLAM: LiDAR+X Dense SLAM with $\sum_{i=0}^{5}C_5^i$ Configurable Sensor Combinations SLAMesh: Real-time lidar simul- taneous localization and meshing,

Reference 41

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source=pdf_text observed=2026-06-29T04:38:21.101435Z digest=sha256:0224fd61b473596665fb73a1fc49b52575f752ca73041602e81af7f934e5c746

Observation d914b232-5a91-4a6b-822a-8e4ed4b594b7 · outbound

This paper cites Symbolic representation and toolkit development of iterated error-state extended kalman filters on manifolds,.

LXD-SLAM: LiDAR+X Dense SLAM with $\sum_{i=0}^{5}C_5^i$ Configurable Sensor Combinations Symbolic representation and toolkit development of iterated error-state extended kalman filters on manifolds,

Reference 42

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Observation 7864a024-1ab1-47fa-8309-11316333b642 · outbound

This paper cites Fast ray-tracing of rectilinear volume data using distance transforms,.

LXD-SLAM: LiDAR+X Dense SLAM with $\sum_{i=0}^{5}C_5^i$ Configurable Sensor Combinations Fast ray-tracing of rectilinear volume data using distance transforms,

Reference 43

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Observation 79f2819e-5712-4298-b920-78da90859587 · outbound

This paper cites Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography,.

LXD-SLAM: LiDAR+X Dense SLAM with $\sum_{i=0}^{5}C_5^i$ Configurable Sensor Combinations Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography,

Reference 44

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Observation 8473e7b9-c4cf-40e0-b1e6-7113564b814f · outbound

This paper cites NTU VIRAL: A Visual-Inertial-Ranging-Lidar Dataset, from an Aerial Vehicle Viewpoint,.

LXD-SLAM: LiDAR+X Dense SLAM with $\sum_{i=0}^{5}C_5^i$ Configurable Sensor Combinations NTU VIRAL: A Visual-Inertial-Ranging-Lidar Dataset, from an Aerial Vehicle Viewpoint,

Reference 45

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Observation 1c8fa3e1-9720-4ec2-b774-f184aa6de771 · outbound

This paper cites FusionPortableV2: A Unified Multi-Sensor Dataset for Generalized SLAM Across Diverse Platforms and Scalable Environments,.

LXD-SLAM: LiDAR+X Dense SLAM with $\sum_{i=0}^{5}C_5^i$ Configurable Sensor Combinations FusionPortableV2: A Unified Multi-Sensor Dataset for Generalized SLAM Across Diverse Platforms and Scalable Environments,

Reference 46

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Observation 82c7f57c-649c-4dfd-935c-7e0d9ec7bb5d · outbound

This paper cites Are we ready for autonomous driving? the KITTI vision benchmark suite,.

LXD-SLAM: LiDAR+X Dense SLAM with $\sum_{i=0}^{5}C_5^i$ Configurable Sensor Combinations Are we ready for autonomous driving? the KITTI vision benchmark suite,

Reference 47

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Observation d68ff18b-0ca3-4b08-8d8f-861e6940654b · outbound

This paper cites SDV-LOAM: Semi- direct visual–lidar odometry and mapping,.

LXD-SLAM: LiDAR+X Dense SLAM with $\sum_{i=0}^{5}C_5^i$ Configurable Sensor Combinations SDV-LOAM: Semi- direct visual–lidar odometry and mapping,

Reference 48

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Observation 35aa0b03-51c2-4cc5-9963-2e5d7cb3ebdf · outbound

This paper cites D-LIOM: Tightly-coupled direct lidar-inertial odometry and mapping,.

LXD-SLAM: LiDAR+X Dense SLAM with $\sum_{i=0}^{5}C_5^i$ Configurable Sensor Combinations D-LIOM: Tightly-coupled direct lidar-inertial odometry and mapping,

Reference 49

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Observation 50709ecd-b683-418d-bf32-6bb5d96dbb5e · outbound

This paper cites Ct-LVI: A framework toward continuous-time laser-visual-inertial odometry and mapping,.

LXD-SLAM: LiDAR+X Dense SLAM with $\sum_{i=0}^{5}C_5^i$ Configurable Sensor Combinations Ct-LVI: A framework toward continuous-time laser-visual-inertial odometry and mapping,

Reference 50

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Observation 492d10de-7c6d-4abb-9bde-68a4655bcadb · outbound

This paper cites Direct lidar-inertial odometry: Lightweight LIO with continuous-time motion correction,.

LXD-SLAM: LiDAR+X Dense SLAM with $\sum_{i=0}^{5}C_5^i$ Configurable Sensor Combinations Direct lidar-inertial odometry: Lightweight LIO with continuous-time motion correction,

Reference 51

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Observation 15d2bb73-7cf3-44e0-94ea-dd973f0e4556 · outbound

This paper cites SR-LIVO: Lidar- inertial-visual odometry and mapping with sweep reconstruction,.

LXD-SLAM: LiDAR+X Dense SLAM with $\sum_{i=0}^{5}C_5^i$ Configurable Sensor Combinations SR-LIVO: Lidar- inertial-visual odometry and mapping with sweep reconstruction,

Reference 52

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source=pdf_text observed=2026-06-29T04:38:21.101435Z digest=sha256:ed1491774fa1479f03d4251ef237894d843b7037b4db4256bf0c78960baa2e59

Observation 9ecc9b52-2eab-401b-bec7-9e37142ffd85 · outbound

This paper cites Scan Context++: Structural place recogni- tion robust to rotation and lateral variations in urban environments,.

LXD-SLAM: LiDAR+X Dense SLAM with $\sum_{i=0}^{5}C_5^i$ Configurable Sensor Combinations Scan Context++: Structural place recogni- tion robust to rotation and lateral variations in urban environments,

Reference 53

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source=pdf_text observed=2026-06-29T04:38:21.101435Z digest=sha256:140e7c1b523f1f213849a7da05320186fa8394739d40fb7cc320c8ee89ce4048

Pith citing papers

No inbound Pith citation observations are available.