When LiDAR Fades, Radar Takes Over: LiDAR Reliability-Aware Adaptive Localization Under Airborne Obscurants

Moumita Mukherjee; Avijit Banerjee; Anton Koval; George Nikolakopoulo

cs.ROOct 9, 2026

When LiDAR Fades, Radar Takes Over: LiDAR Reliability-Aware Adaptive Localization Under Airborne Obscurants

Authors: Moumita Mukherjee, Avijit Banerjee, Anton Koval, George Nikolakopoulo

Organizations: Robotics and AI Group, Department of Computer, Electrical and Space Engineering, Luleå University of Technology, Luleå, Sweden

Abstract

LiDAR-inertial odometry (LIO) is widely used for real-time localization and mapping, but its reliability can degrade substantially in subterranean environments affected by smoke, dust, and other airborne obscurants, whereas radar remains comparatively robust. This article presents a one-class temporal learning framework for LiDAR degradation detection and reliability-aware localization under such conditions. A broad set of degradation-sensitive cues, including spatial, cross-modal, geometric/motion, intensity, and measurement-recency features, is evaluated to characterize changes in LiDAR sensing quality. Their temporal evolution is modelled over sliding windows using an LSTM autoencoder trained exclusively on nominal operation, enabling deviations from healthy sensor behaviour to be detected without requiring labelled degradation data. The resulting LiDAR reliability estimate governs adaptive selection between LIO and radar inertial odometry (RIO), retaining LIO during reliable sensing and transferring localization authority to RIO when degradation is detected. The framework is evaluated using real-world ground robot data collected in GNSS-denied subterranean tunnels under nominal and dense-smoke conditions. Experimental results demonstrate degradation detection, adaptive modality switching, and continued localization output under severe LiDAR degradation. A feature family ablation study further shows that a reduced feature representation can improve degradation-aware localization performance in the evaluated dense-smoke scenarios, highlighting the importance of feature parsimony in one-class sensor degradation detection. The code will be released as open source to support reproducibility and further research.

Figures & tables

Figure 1: Conceptual framework for LiDAR degradation-aware adaptive localization under airborne obscurants.

Figure 2: Proposed reliability aware LMRIO framework. Asynchronous LiDAR, IMU, LIO, and RIO measurements are temporally synchronized and encoded into a Twelve dimensional degradation sensitive feature representation( $f_{\eta}$ ). Consecutive normalized feature vectors( $\bar{f}_{\eta}$ ) form a temporal sequence processed by an LSTM autoencoder trained on nominal operation. Reconstruction errors are used to infer LiDAR degradation and adaptively select between LIO and RIO.

Figure 3: Localization trajectories and LiDAR maps for the P-shaped and round-tunnel traversals. Under nominal conditions, LMRIO closely follows the LIO estimate in both trajectories (a,c). In dense smoke, the P-shaped traversal exhibits pronounced LIO divergence as LiDAR quality deteriorates (b), while the round-tunnel traversal shows stale LiDAR measurements and loss of LiDAR-based localization updates (d). In both degraded cases, LMRIO switches to RIO during intervals identified as unreliable, maintaining a consistent localization output.

Figure 4: Trajectory comparison across the four experimental scenarios. Under nominal conditions, LMRIO closely follows LIO for both the P-shaped and round-tunnel traversals (a,c). Under dense smoke, the P-shaped traversal exhibits severe LIO divergence following LiDAR degradation (b), while the round-tunnel traversal shows stale LiDAR-based localization (d). In both degraded cases, LMRIO selects RIO during unreliable LiDAR operation, preserving a continuous localization output.

Figure 5: Raw and smoothed LSTM-autoencoder reconstruction errors with the nominal threshold. Nominal traversals remain largely below the threshold despite transient fluctuations (a,c), whereas dense-smoke traversals show pronounced or sustained error growth associated with LiDAR degradation (b,d). The smoothed error drives learned degradation detection, while LiDAR recency is evaluated independently.

Figure 6: Estimator-selection traces across the four experimental scenarios. LIO remains selected throughout nominal operation (a,c). Under dense smoke, the degradation detector triggers a transition to RIO in the P-shaped traversal (b), while the LiDAR-recency criterion activates RIO when measurements become stale in the round-tunnel traversal (d). The switching variables $\Delta_{\mathrm{LIO}}$ and $\Delta_{\mathrm{RIO}}$ indicate the active localization modality.

Parameter	Value
Laser Channels	16
Vertical Resolution	0.2°
Field of View (FOV)	360°
Range	100m
Rotation Rate	(1-100) Hz
Points per second	300,000 points/sec

Table I : Configuration Parameters for 3D-Velodyne LiDAR

Parameter	Value
Operating frequency range	60–64 GHz
Available RF bandwidth	up to 4 GHz
Maximum IF bandwidth	10 MHz
ADC sampling rate	up to 25 Msps
Number of transmitters	3
Number of receivers	4

Table II : Configuration Parameters for the TI-IWR6843AoP Millimeter-Wave Radar

Variant	LIO usage [%]	RIO usage [%]	First switch [s]	LiDAR Stale [s]	$\bm{\Delta t_{\mathrm{stale}}}$ [s]	LMRIO-LIO RMSE [m]	LMRIO-RIO RMSE [m]
$C_{1}$	28.21	71.80	78.00	130	52.00	$1.816\times 10^{4}$	1.1148
$C_{2}$	45.30	54.70	125.00	130	5.00	$1.816\times 10^{4}$	1.9943
$C_{3}$	28.47	71.54	78.27	130	51.73	$1.816\times 10^{4}$	1.1287
$C_{4}$	27.07	72.93	74.44	130	55.56	$1.816\times 10^{4}$	1.0553
$C_{5}$	28.11	71.89	80.12	130	49.88	$1.816\times 10^{4}$	2.1200
$C_{6}$	27.88	72.12	76.66	130	53.34	$1.816\times 10^{4}$	1.0977

Table III : Ablation results for the P-shaped dense-smoke traversal. The reconstruction-error-based switching time closest to the age criterion staleness time is highlighted.

Variant	Removed cues	Dim.
Twelve Feature( $C_{1}$ )	None	12
No Spatial( $C_{2}$ )	$N_{\eta}^{\mathcal{L}},\;N_{\eta}^{v},\;\mu_{\eta}^{d},\;\tilde{d}_{\eta},\;\sigma_{\eta}^{d},\;\mathrm{IQR}_{\eta}^{d}$	6
No Cross-modal( $C_{3}$ )	$\Delta p_{k_{\eta}}$	11
No Geom./Motion( $C_{4}$ )	$\Delta c_{\eta},\;\omega_{k_{\eta}}^{I}$	10
No Intensity ( $C_{5}$ )	$\tilde{I}_{\eta},\;\mathrm{IQR}_{\eta}^{I}$	10
No LiDAR age ( $C_{6}$ ) ∗	Explicit LiDAR-age decision rule	12

Table IV : Feature-family configurations used in the ablation study.

Condition	Trajectory pair	RMSE [m]
Nominal	LMRIO-LIO	0.0121
Nominal	RIO-LIO	0.0650
Dense smoke	LMRIO-RIO	1.1148
Dense smoke	LIO-LMRIO	$1.816\times 10^{4}$

Table V : Pairwise RMSE Between Localization Trajectories in the P-Shaped Tunnel

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