NeRF-LOAM: Neural Implicit Representation for Large-Scale Incremental LiDAR Odometry and Mapping (2303.10709v1)

Abstract: Simultaneously odometry and mapping using LiDAR data is an important task for mobile systems to achieve full autonomy in large-scale environments. However, most existing LiDAR-based methods prioritize tracking quality over reconstruction quality. Although the recently developed neural radiance fields (NeRF) have shown promising advances in implicit reconstruction for indoor environments, the problem of simultaneous odometry and mapping for large-scale scenarios using incremental LiDAR data remains unexplored. To bridge this gap, in this paper, we propose a novel NeRF-based LiDAR odometry and mapping approach, NeRF-LOAM, consisting of three modules neural odometry, neural mapping, and mesh reconstruction. All these modules utilize our proposed neural signed distance function, which separates LiDAR points into ground and non-ground points to reduce Z-axis drift, optimizes odometry and voxel embeddings concurrently, and in the end generates dense smooth mesh maps of the environment. Moreover, this joint optimization allows our NeRF-LOAM to be pre-trained free and exhibit strong generalization abilities when applied to different environments. Extensive evaluations on three publicly available datasets demonstrate that our approach achieves state-of-the-art odometry and mapping performance, as well as a strong generalization in large-scale environments utilizing LiDAR data. Furthermore, we perform multiple ablation studies to validate the effectiveness of our network design. The implementation of our approach will be made available at https://github.com/JunyuanDeng/NeRF-LOAM.

• The paper introduces a framework that combines neural implicit representations with LiDAR data to achieve high-fidelity odometry and mapping in large-scale outdoor settings.
• It employs a neural signed distance function to stabilize 6-DoF pose estimation by separating ground from non-ground points, reducing Z-axis drift.
• Experimental results on datasets like KITTI, Newer College, and MaiCity demonstrate state-of-the-art accuracy in both mapping and odometry performance.

NeRF-LOAM: Neural Implicit Representation for Large-Scale Incremental LiDAR Odometry and Mapping

The paper introduces NeRF-LOAM, a novel approach for simultaneously achieving LiDAR odometry and mapping (LOAM) using neural implicit representations, specifically for large-scale outdoor environments. The proposed method integrates the advancements in neural radiance fields (NeRF) for implicit 3D scene representation with incremental LiDAR data, addressing a significant gap in existing methodologies that tend to prioritize tracking accuracy over dense environment reconstruction.

Core Components and Methodology

The NeRF-LOAM framework consists of three primary modules: neural odometry, neural mapping, and mesh reconstruction. Each module is crucial for the holistic operation of the system:

1. Neural Odometry: This module estimates the 6-degree-of-freedom (DoF) pose of a mobile system using LiDAR data. It employs a neural signed distance function (SDF) to mitigate Z-axis drift by segregating ground and non-ground points. This separation ensures a more stable and accurate odometric estimation, leveraging the structured nature of ground points for enhanced precision.
2. Neural Mapping: After odometry estimation, the mapping module transforms LiDAR points into the global coordinate system, utilizing octree-based voxel embeddings. The system dynamically generates these embeddings without pre-allocation, allowing the method to be pre-training free and adaptable across different environments. Joint optimization of voxel embeddings and pose ensures high-fidelity 3D representation.
3. Mesh Reconstruction: This final module produces a dense mesh map using the marching cubes algorithm, based on the SDF values optimized during the mapping process. The inclusion of a key-scan refinement strategy ensures that the map is both accurate and consistent over large-scale environments, addressing potential inconsistencies from trajectory drift.

Experimental Evaluation and Results

The NeRF-LOAM approach demonstrates state-of-the-art performance across several publicly available datasets, including MaiCity, Newer College, and KITTI. These experiments assess both odometry accuracy and mapping quality. Notably, the system achieves:

• Odometry: Effective tracking without the need for pre-training, showing competitive results against established methods such as ICP variants, SuMA, and others.
• Mapping: Superior reconstruction metrics, including accuracy, completion, and Chamfer-L1 distance, particularly when ground truth poses are available. The approach excels in producing smooth, complete maps even under sparse observation conditions as seen in the Newer College dataset.

Implications and Future Directions

The implications of NeRF-LOAM are significant, both practically and theoretically. Practically, the method offers a robust solution for autonomous navigation systems that require accurate mapping and localization in diverse, large-scale environments. Theoretically, the integration of neural implicit representations with LiDAR data opens new avenues for research in 3D scene understanding and SLAM systems.

Future work could focus on enhancing real-time processing capabilities by optimizing the intersection query operations and considering loop closure techniques to reduce trajectory drift in long-term operations. Additionally, potential applications in augmented reality and robotics present exciting possibilities for further exploration.

Conclusion

NeRF-LOAM represents a noteworthy advance in LiDAR-based odometry and mapping. By leveraging neural implicit representations, the approach not only attains high accuracy in pose estimation but also facilitates detailed environmental reconstruction. This work addresses critical challenges in large-scale autonomy and sets a foundational platform for future innovations in the domain.