Implicit LiDAR SLAM with Confidence-Guided SDF and Normal-Driven Sampling

Abstract

Implicit representations for LiDAR-based Simultaneous Localization and Mapping (SLAM) offer significant advantages in storage efficiency and expressive power over traditional explicit maps. However, a critical limitation for implicit SLAM is their deterministic nature, which prevents the quantification of prediction uncertainty in sparse or noisy conditions. Furthermore, the accuracy of the underlying Signed Distance Field (SDF) is often compromised by systematic errors arising from the angular dependency of LiDAR measurements, where oblique incident angles lead to biased distance estimations and degrade map quality. To address these challenges, this paper introduces a framework that enhances the robustness and accuracy of implicit LiDAR SLAM by integrating uncertainty estimation and an adaptive sampling strategy. We propose a neural network-based approach to learn and predict SDF uncertainty, which is then effectively incorporated into both localization and mapping processes. Concurrently, to mitigate incident angle-induced errors, we develop an adaptive sampling scheme that weights LiDAR rays based on surface normal information. Validation on public datasets and a custom experimental platform demonstrates that our approach outperforms baseline methods in terms of localization, mapping accuracy, and robustness.