SKiD-SLAM: Resource-Aware Distributed Multi-Robot LiDAR SLAM for Planetary Missions with Field Deployments at Analog Sites

Hogyun Kim; Juwon Kim; Dongjin Cho; Jiwon Choi; Geonmo Yang; Seokhwan Jeong; Jiyun Lee; Miryeong Park

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

\alpha

,

\beta

, and

\gamma

, respectively. In the distributed case, robot

\alpha

receives shared data from robots

\beta

and

\gamma

. (a) LIO-SAM-based pose estimation [ 71 ] using LiDAR-IMU fusion. (b) Inter-Robot Place Recognition using SOLiD [ 42 ] descriptors from robot

\alpha

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

\alpha

.

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] $\downarrow$	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,

M_{\text{total}}

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] $\downarrow$	RRE [ ∘ ] $\downarrow$	Success rate [%] $\uparrow$
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	$\boldsymbol{0.196}$	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] $\downarrow$
Method	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 $\alpha$	robot $\beta$	robot $\alpha$	robot $\beta$	robot $\alpha$	robot $\beta$	robot $\alpha$	robot $\beta$
DiSCo-SLAM [ 30 ]	$0.752\pm 0.029$	$16.938\pm 0.033$	N/A	N/A	$0.137\pm 0.008$	$40.924\pm 0.013$	$\boldsymbol{0.316\pm 0.018}$	$2.380\pm 0.051$
DCL-SLAM [ 92 ]	$\boldsymbol{0.318\pm 0.021}$	$5.551\pm 0.041$	N/A	N/A	$\boldsymbol{0.120\pm 0.007}$	$1.851\pm 0.013$	$0.364\pm 0.022$	$5.674\pm 0.068$
SKiD-SLAM	$0.339\pm 0.024$	$\boldsymbol{0.403\pm 0.038}$	N/A	N/A	$0.198\pm 0.010$	$\boldsymbol{1.046\pm 0.014}$	$0.546\pm 0.026$	$\boldsymbol{0.080\pm 0.006}$

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{,}000\,\text{kbps}

, (c)

500\,\text{kbps}

, and (d)

250\,\text{kbps}

. Our SKiD-SLAM merges the maps consistently down to

250\,\text{kbps}

, 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

250\,\text{kbps}

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] $\downarrow$	9,600	230,400	1,585	320
Bandwidth ( $1{,}000\,\text{kbps}$ )	61.27	88.64	58.93	53.18
Bandwidth ( $500\,\text{kbps}$ )	77.53	96.38	66.26	62.65
Bandwidth ( $250\,\text{kbps}$ )	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^{\circ}

(10

\%

of a full

360^{\circ}

scan), (b)

72^{\circ}

(20

\%

), and (c)

108^{\circ}

(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:

\alpha

,

\beta

, and

\gamma

) and (b) the KICT planetary analog terrain (two robots:

\alpha

and

\beta

).

Figure 23: Representative scenes from the field deployments at the Majang lava-tube cave and the KICT planetary analog terrain.

SKiD-SLAM: Resource-Aware Distributed Multi-Robot LiDAR SLAM for Planetary Missions with Field Deployments at Analog Sites

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