| ICP |
Besl & McKay 1992 |
Iterative Closest Point, closest-point correspondence, closed-form rigid transform, local convergence (needs initialization), foundation of 3D-3D registration |
| DTAM |
Newcombe 2011 |
→ see Level 3 Direct SLAM |
| KinectFusion |
Newcombe 2011 |
GPGPU, Tracking (project depth → 3D, surface normal, coarse-to-fine ICP), Mapping (volumetric integration, TSDF), Robust to small scene changes, Cannot model deformation, Map growth cubic, Room-size only |
| Double Window Optimisation |
Strasdat 2011 |
Inner window (local BA) + outer window (pose graph), covisibility graph, constant-time optimization |
| Kintinuous |
Whelan 2012 |
Volume shift, Geometric, Photometric, dBoW+SURF, Optimization, Loop closure |
| RGBD-SLAM-V2 |
Endres 2013 |
Tracking (color image, visual features, depth image, point cloud, transformation), Mapping (OctoMap 2013) |
| SLAM++ |
Salas-Moreno 2013 |
Object-oriented SLAM |
| DVO |
Kerl 2013 |
Keyframe, Depth, Direct method, Optimization, Loop closure |
| RTAB-Map |
Labbé 2014 |
Loop closure, Map merge, Multi-session memory management |
| MRS-Map |
Stückler 2014 |
Multi-resolution surfel maps in an octree, shape + color statistics per surfel, noise-aware RGB-D registration, real-time on CPU |
| ElasticFusion |
Whelan 2015 |
Active: frame-to-model tracking (photometric + geometric), joint optimization, fused surfel-based model reconstruction · Inactive: local loop closure (model-to-model local surface, submodel separation), global loop closure (randomised fern encoding, non-rigid space deformation) |
| DynamicFusion |
Newcombe 2015 |
6D motion field, Deformable scene |
| ORB-SLAM2 (RGB-D mode) |
Mur-Artal 2017 |
Bundle adjustment, Sparse reconstruction (→ also in Level 3) |
| BundleFusion |
Dai 2016 |
Local-to-global optimization, Sparse RGB feature, Coarse global pose estimation, Fine pose refinement (geometric + photometric) |
| SemanticFusion |
McCormac 2016 |
Deep Learning CNN, Deep Semantic SLAM |
| InfiniTAM v3 |
Prisacariu 2017 |
Tracking (scene raycast, depth image, RGB image), Relocalization (random ferns), Mapping (TSDF reconstruction, voxel hashing, surfel reconstruction) |
| Fusion++ |
McCormac & Clark 2018 |
Deep Learning CNN, Mask-RCNN instance segmentation, Object-level SLAM, No prior, Object-level TSDF reconstruction |
| PointFusion / DenseFusion |
Xu 2018 / Wang 2019 |
RGB-D object 6-DoF pose estimation, point cloud + image feature fusion (object frontend for object-level SLAM) |
| BAD SLAM |
Schöps 2019 |
Direct RGB-D bundle adjustment, surfel map, real-time GPU BA, ETH3D benchmark |
| RTAB-Map (RGB-D / LiDAR) |
Labbé 2019 |
Multi-sensor RGB-D/LiDAR support, light-source detection (2016) |
| MoreFusion |
Wada 2020 |
DL instance segmentation, Object-level volumetric fusion, Volumetric pose prediction, 3D scene reconstruction, Collision-based refinement, Semantic SLAM, Object pose estimation, CAD object fitting |
| NodeSLAM |
Sucar 2020 |
Occupancy VAE, Object-level SLAM (→ also in Level 5 Latent Representation) |
| DSP-SLAM |
Wang (UCL) 2021 |
DeepSDF shape prior + ORB-SLAM2, object-level dense reconstruction (mono/stereo/LiDAR) |