SKiD-SLAM: Resource-Aware Distributed Multi-Robot LiDAR SLAM for Planetary Missions with Field Deployments at Analog Sites
Authors: Hogyun Kim, Juwon Kim, Dongjin Cho, Jiwon Choi, Geonmo Yang, Seokhwan Jeong, Jiyun Lee, Miryeong Park, Hyungtae Lim, Younggun Cho
Organizations: Department of Electrical and Computer Engineering, Inha University, Incheon 22212, Republic of Korea · Laboratory for Information & Decision Systems (LIDS), Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA
Future planetary exploration missions require teams of robots to collaboratively map unknown environments under severe communication limits, constrained onboard computation, and no central infrastructure. In this field report, we present SKiD-SLAM, a distributed multi-robot LiDAR simultaneous localization and mapping (SLAM) system for such environments, and report its deployment across two mission concepts: cave exploration and scientific surface exploration. We survey the communication and computation budgets of actual planetary missions and select modules that fit these budgets for inter-robot place recognition, relative pose estimation, and distributed trajectory fusion. Before field deployment, we verify the assembled system on public datasets resembling planetary environments and in a simulator built on NASA JPL's Open Source Rover. We then validate SKiD-SLAM through field deployments in South Korea: a team of wheeled rovers explores the Majang lava-tube cave on Jeju Island, and quadruped robots carry out a crater-and-rock science scenario on a planetary analog terrain at the Korea Institute of Civil Engineering and Building Technology (KICT) in Yeoncheon. Across both deployments, SKiD-SLAM reliably performed distributed multi-robot LiDAR SLAM within a 250 kbps inter-robot communication budget, demonstrating its feasibility under challenging field conditions. Supplementary materials, including code and field data, are available at https://sparolab.github.io/research/skid_slam/.
Figures & tables
Figure 1: Field deployment at the Majang lava-tube cave on Jeju Island. The cave floor is covered with loose rocks and rubble, and its geometrically self-similar walls receive no natural light, conditions representative of planetary lava tubes. We deploy wheeled rovers to perform distributed multi-robot SLAM in this GPS-denied environment.
Figure 2: Field deployment at the KICT planetary analog terrain in Yeoncheon. The surface is shaped by numerous impact-crater-like depressions and scattered rocks that emulate the open surface conditions of the Moon. We deploy quadruped robots to perform distributed multi-robot SLAM across this cratered terrain.
Figure 3: Robot platforms and sensors, i.e., LiDAR, IMU, camera, and thermal camera, used in our field experiments. (Left) A wheeled rover deployed for cave exploration at the Majang lava-tube cave on Jeju Island. (Right) A quadruped robot deployed for scientific surface exploration at the planetary analog testbed in Yeoncheon. Note that the camera and thermal camera are used only for visual inspection and are not used by our SKiD-SLAM pipeline in this field report.
Figure 4: Our Rerun-based viewer [ 65 ] for monitoring robot sensor data at an experimental monitoring workstation. The station communicates with the robots over a Wi-Fi access point to stream their data in real time. Here, two robots are deployed. (Top) Scan data from robot 1, shown with the corresponding camera image. (Bottom) Scan data from robot 2, shown with the corresponding camera image. The camera images are provided only for easier visual inspection and are not used by our algorithm, which relies solely on the LiDAR sensor.
Figure 5: Representative milestones in planetary robotic exploration, showing a progression from isolated single-robot missions toward cooperative multi-robot teams. From left to right, the deployment paradigm shifts from independent operation, exemplified by NASA’s Spirit and Opportunity [ 14 ] and Perseverance [ 21 ] , toward cooperative autonomy, exemplified by the German Aerospace Center (DLR) and European Space Agency (ESA)’s ARCHES [ 69 ] and NASA’s CADRE [ 60 , 17 , 64 ] .
Figure 6: Overview of our distributed LiDAR SLAM framework, called SKiD-SLAM . Building on SOLiD [ 42 ] and KISS-Matcher [ 55 ] , our SKiD-SLAM consists of five key components. The three robots are denoted by α , β , and γ , respectively. In the distributed case, robot α receives shared data from robots β and γ . (a) LIO-SAM-based pose estimation [ 71 ] using LiDAR-IMU fusion. (b) Inter-Robot Place Recognition using SOLiD [ 42 ] descriptors from robot α and associated robots. (c) Coarse-to-fine inter-robot registration using KISS-Matcher and Small-GICP [ 45 ] for coarse and fine alignment, respectively. (d) Two-stage outlier rejection, where truncated mean squared error (MSE) is used to verify the validity of each loop individually, and PCM [ 59 ] is used to verify the geometric consistency of loops across different trajectories. (e) Final distributed multi-robot mapping result centered on robot α .
Figure 7: Overview of SOLiD descriptor construction [ 42 ] . Assuming the green-shaded regions represent occlusion or erroneous measurements caused by reflections of laser rays, directly using raw bin counts in such regions could lead to perceptual aliasing, whereas SOLiD mitigates this through the dot product between REC and IEV.
Figure 8: Preliminary-evaluation environments matched to our target mission settings. (Left) The Tunnel sequence from the GEODE dataset, representing the subterranean cave setting. (Right) Our custom Gazebo-based Moon simulator built on NASA JPL’s Open Source Rover, representing cratered planetary analog terrain; the inset shows the simulated rover.
Sequence
Scan Context [ 39 ]
LiDAR Iris [ 83 ]
OverlapTransformer [ 58 ]
LoGG3D-Net [ 81 ]
SOLiD [ 42 ]
Descriptor size [B] ↓
9,600
230,400
2,048
2,048
320
Tunnel
48.506 MB
1.164 GB
10.347 MB
10.347 MB
1.617 MB
Moon
29.474 MB
707.379 MB
6.288 MB
6.288 MB
0.982 MB
Table 1: Comparison of total descriptor memory usage, Mtotal in ( 13 ), shared during multi-robot map merging on the Tunnel and Moon sequences.
Figure 9: Precision-recall (PR) curves for inter-robot place recognition on the (left) Tunnel and (right) Moon sequences, corresponding to the cave and cratered surface settings.
Method
RTE [cm] ↓
RRE [ ∘ ] ↓
Success rate [%] ↑
ICP [ 66 ]
173.96
2.183
2.01
PLANE-ICP [ 56 ]
136.58
2.334
6.61
GICP [ 70 ]
8.98
0.203
83.33
Small-GICP [ 45 ]
8.98
0.203
83.33
V-GICP [ 44 ]
9.24
0.196
82.47
NDT [ 75 ]
N/A
N/A
0.00
Table 2: Scan-level registration performance evaluated in the Moon environment.
Figure 10: Registration success rate on the Moon sequence under synthetic relative rotations.
Method
Runtime [s] ↓
Min
Mean
Median
Max
ICP [ 66 ]
0.002
0.005
0.004
0.093
Plane-ICP [ 56 ]
0.003
0.005
0.005
0.020
GICP [ 70 ]
0.003
0.005
0.005
0.012
Small-GICP [ 45 ]
0.003
0.005
0.005
0.020
VGICP [ 44 ]
0.003
0.004
0.004
0.009
Table 3: Runtime of point cloud registration methods on an Intel NUC 11 Pro evaluated on the Moon sequence.
Datasets
Tunnel
Moon
Evaluation
ATE [m]
ARE [ ∘ ]
ATE [m]
ARE [ ∘ ]
Robot Name
robot α
robot β
robot α
robot β
robot α
robot β
robot α
robot β
DiSCo-SLAM [ 30 ]
0.752±0.029
16.938±0.033
N/A
N/A
0.137±0.008
40.924±0.013
0.316±0.018
2.380±0.051
DCL-SLAM [ 92 ]
0.318±0.021
5.551±0.041
N/A
N/A
0.120±0.007
1.851±0.013
0.364±0.022
5.674±0.068
SKiD-SLAM
0.339±0.024
0.403±0.038
N/A
N/A
0.198±0.010
1.046±0.014
0.546±0.026
0.080±0.006
Table 4: Comparison of absolute translation error (ATE) and absolute rotation error (ARE) using distributed LiDAR SLAM approaches. For the Tunnel sequence from the GEODE dataset, rotation ground truth is not provided; therefore, the ARE values are marked as N/A, indicating that we cannot evaluate them.
Figure 11: Estimated trajectories from DiSCo-SLAM [ 30 ] , DCL-SLAM [ 92 ] , and SKiD-SLAM compared with ground truth on the Tunnel and Moon sequences.
Sequence
# of Robots
Duration [s]
Length [m]
# of Keyframes
Majang lava-tube cave
3
227.4 / 153.3 / 343.1
24.4 / 18.8 / 50.3
102 / 52 / 156
KICT planetary analog terrain
2
443.9 / 519.8
225.2 / 200.0
431 / 468
Table 5: Scale of the field deployments, reporting the number of robots and the per-robot operating duration, trajectory length, and number of keyframes for each sequence.
Figure 12: Qualitative multi-robot map merging results on the Tunnel sequence. (a) DiSCo-SLAM and (b) DCL-SLAM leave visible map misalignments, whereas (c) SKiD-SLAM produces a consistent merged map.
Figure 13: Qualitative multi-robot map merging results on the Moon sequence. (a) DiSCo-SLAM and (b) DCL-SLAM leave visible map misalignments, whereas (c) SKiD-SLAM produces a consistent merged map.
Figure 14: Qualitative multi-robot map merging results at the Majang lava-tube cave. (a) DiSCo-SLAM, (b) DCL-SLAM, and (c) Multi-Proxy fail to merge the robots’ maps consistently, whereas (d) SKiD-SLAM produces a coherent global map of the cave.
Figure 15: Qualitative multi-robot map merging results at the KICT planetary analog terrain in Yeoncheon. (a) DiSCo-SLAM produces misaligned maps, whereas (b) DCL-SLAM, (c) Multi-Proxy, and (d) SKiD-SLAM all successfully merge the robots’ maps into coherent global maps.
Figure 16: Map merging of SKiD-SLAM at the Majang lava-tube cave under bandwidth limits imposed via NetEm [ 28 ] . (a) Raw communication without a bandwidth limit; limits of (b) 1,000kbps , (c) 500kbps , and (d) 250kbps . Our SKiD-SLAM merges the maps consistently down to 250kbps , the inter-robot bandwidth budget assumed in Sec. 2.2.2 .
Figure 17: Multi-robot map merging at the KICT planetary analog terrain under a 250kbps bandwidth limit. (a) DiSCo-SLAM, (b) DCL-SLAM, and (c) Multi-Proxy fail under this budget, whereas (d) our SKiD-SLAM completes merging under the same limit.
Method
DiSCo-SLAM [ 30 ]
DCL-SLAM [ 92 ]
Multi-Proxy [ 82 ]
Proposed
Avg. transmitted message size [B] ↓
9,600
230,400
1,585
320
Bandwidth ( 1,000kbps )
61.27
88.64
58.93
53.18
Bandwidth ( 500kbps )
77.53
96.38
66.26
62.65
Bandwidth ( 250kbps )
93.34
99.71
92.52
73.18
Table 6: Average transmitted message size and scan drop ratio (%) under the network degradation condition, i.e., bandwidth limitation, on the KICT planetary analog terrain. Each size is measured as the serialized ROS message that the method’s implementation transmits for inter-robot association, which is a float64 matrix or vector descriptor for DiSCo-SLAM, DCL-SLAM, and our SKiD-SLAM, and a descriptor carried in a point cloud message for Multi-Proxy, as its standard implementation transmits its proxy descriptor in point cloud form. A lower value indicates better communication robustness.
Figure 18: Component ablation study on the Majang lava-tube cave. Each panel disables one module: (a) SOLiD replaced by Scan Context, (b) without KISS-Matcher, (c) without t-MSE, and (d) without PCM. Removing SOLiD causes a clear failure and removing PCM degrades the merge, whereas the other modules have a smaller effect in this environment.
Figure 19: Component ablation study on the KICT planetary analog terrain. Each panel disables one module: (a) SOLiD replaced by Scan Context, (b) without KISS-Matcher, (c) without t-MSE, and (d) without PCM. Removing either KISS-Matcher (b) or PCM (d) causes the merge to fail, showing that the decisive component differs from the cave environment.
Figure 20: Our SKiD-SLAM performance on the Aerial sequence with point cloud clipping to simulate ultra-low overlap. The point clouds are cropped to forward-facing FOVs of (a) 36∘ (10 % of a full 360∘ scan), (b) 72∘ (20 % ), and (c) 108∘ (30 % ).
Figure 21: Robustness evaluation of SKiD-SLAM under network impairments at the KICT planetary analog terrain using NetEm. (a) With 2 ms delay, maps merge successfully. (b) With 200 ms delay, map merging fails due to delayed loop closure detection. (c) With 50% packet loss, redundant keyframe exchange enables successful merging. (d) With 75% packet loss, insufficient data prevents merging.
Figure 22: Per-keyframe processing time of SKiD-SLAM for each robot on the embedded onboard computer at the two field sites: (a) the Majang lava-tube cave (three robots: α , β , and γ ) and (b) the KICT planetary analog terrain (two robots: α and β ).
Figure 23: Representative scenes from the field deployments at the Majang lava-tube cave and the KICT planetary analog terrain.
We introduce a data-efficient distributed Simultaneous Localization and Mapping (SLAM) framework for multi-robot operation in GPS-denied and communication-constrained environments. The framework is designed for teams of robots equipped with LiDAR, cameras, and inertial sensors, where robots may operate from independently initialized local coordinate frames and share only limited spatial overlap. Our approach constructs a scene graph by using fused RGB-LiDAR point clouds to generate both a semantically segmented point cloud layer, and a layer of discrete bounded objects, to accompany estimated robot trajectories. Scene graph matching is performed collaboratively through exchanging and matching object data with neighboring robots. To maximize communication efficiency, we utilize a multi-step data exchange and optimization process. We demonstrate the effectiveness and communication efficiency of the proposed approach using real-world datasets containing limited trajectory overlap, viewpoint variation, repeated structures, and degraded visual conditions.
Yewei Huang, Tixiao Shan, Abhinav Rajvanshi +4
Dartmouth College, 100 N Main St, Hanover, NH 03755, USA · SRI International, 201 Washington Rd, Princeton, NJ 08540, USA · Stevens Institute of Technology, 1 Castle Point Terrace, Hoboken, NJ 07030, USA
We present a communication-free method for safe multi-robot coordination in complex environments such as forests with dense canopy cover, where GNSS is unavailable. Our approach relies on an onboard anisotropic 3D LiDAR sensor used for SLAM as well as for detecting obstacles and neighboring robots. We develop a novel perception-aware 3D navigation framework that enables robots to safely and effectively progress toward a goal region despite limited sensor field-of-view. The approach is evaluated through extensive simulations across diverse scenarios and validated in real-world field experiments, demonstrating its scalability, robustness, and reliability.
Manuel Boldrer, Michal Kamler, Afzal Ahmad +1
Department of Cybernetics, Czech Technical University in Prague, Karlovo namesti 13, 121 35 Prague 2, Czechia.
Collaborative Simultaneous Localization and Mapping (C-SLAM) is a fundamental capability for multi-robot teams as it enables downstream tasks like planning and navigation. However, existing C-SLAM back-end algorithms that are required to solve this problem struggle to address the practical realities of real-world deployments (i.e. communication limitations, outlier measurements, and online operation). In this paper we propose Robust Incremental Manifold Edge-based Separable ADMM (riMESA) -- a robust, incremental, and distributed C-SLAM back-end that is resilient to outliers, reliable in the face of limited communication, and can compute accurate state estimates for a multi-robot team in real-time. Through the development of riMESA, we, more broadly, make an argument for the use of Consensus Alternating Direction Method of Multipliers as a theoretical foundation for distributed optimization tasks in robotics like C-SLAM due to its flexibility, accuracy, and fast convergence. We conclude this work with an in-depth evaluation of riMESA on a variety of C-SLAM problem scenarios and communication network conditions using both synthetic and real-world C-SLAM data. These experiments demonstrate that riMESA is able to generalize across conditions, produce accurate state estimates, operate in real-time, and outperform the accuracy of prior works by a factor >7x on real-world datasets.
Daniel McGann, Michael Kaess
The authors are with the Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, USA.