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Hi-LOAM, a novel LiDAR odometry and mapping framework, represents scenes using hierarchical implicit neural fields stored in multi-level hash tables organized by an octree. This allows for high reconstruction fidelity in large-scale environments without relying on explicit supervision. By employing a self-supervised, correspondence-free scan-to-implicit matching paradigm for pose estimation, Hi-LOAM achieves state-of-the-art performance on real-world and synthetic datasets.
LiDAR-based SLAM gets a fidelity and generalization boost from Hi-LOAM's hierarchical implicit neural representation, outperforming existing methods without needing pre-training or supervision.
LiDAR Odometry and Mapping (LOAM) is a pivotal technique for embodied-AI applications such as autonomous driving and robot navigation. Most existing LOAM frameworks are either contingent on the supervision signal, or lack of the reconstruction fidelity, which are deficient in depicting details of large-scale complex scenes. To overcome these limitations, we propose a multi-scale implicit neural localization and mapping framework using LiDAR sensor, called Hi-LOAM. Hi-LOAM receives LiDAR point cloud as the input data modality, learns and stores hierarchical latent features in multiple levels of hash tables based on an octree structure, then these multi-scale latent features are decoded into signed distance value through shallow Multilayer Perceptrons (MLPs) in the mapping procedure. For pose estimation procedure, we rely on a correspondence-free, scan-to-implicit matching paradigm to estimate optimal pose and register current scan into the submap. The entire training process is conducted in a self-supervised manner, which waives the model pre-training and manifests its generalizability when applied to diverse environments. Extensive experiments on multiple real-world and synthetic datasets demonstrate the superior performance, in terms of the effectiveness and generalization capabilities, of our Hi-LOAM compared to existing state-of-the-art methods.