Large-scale 3D reconstruction is critical in the field of robotics, and the potential of 3D Gaussian Splatting (3DGS) for achieving accurate object-level reconstruction has been demonstrated. However, ensuring geometric accuracy in outdoor and unbounded scenes remains a significant challenge. This study introduces LI-GS, a reconstruction system that incorporates LiDAR and Gaussian Splatting to enhance geometric accuracy in large-scale scenes. 2D Gaussain surfels are employed as the map representation to enhance surface alignment. Additionally, a novel modeling method is proposed to convert LiDAR point clouds to plane-constrained multimodal Gaussian Mixture Models (GMMs). The GMMs are utilized during both initialization and optimization stages to ensure sufficient and continuous supervision over the entire scene while mitigating the risk of over-fitting. Furthermore, GMMs are employed in mesh extraction to eliminate artifacts and improve the overall geometric quality. Experiments demonstrate that our method outperforms state-of-the-art methods in large-scale 3D reconstruction, achieving higher accuracy compared to both LiDAR-based methods and Gaussian-based methods with improvements of 52.6% and 68.7%, respectively.

Simultaneous localization and mapping (SLAM) is one of the crucial techniques applied in autonomous robot navigation. The majority of present popular SLAM algorithms are built within probabilistic optimization frameworks, achieving high accuracy performance at the expense of high power consumption and latency. In contrast to robots, animals are born with the capability to efficiently and robustly navigate in nature, and bionic SLAM algorithms have received increasing attention recently. Current bionic SLAM algorithms, including RatSLAM, with relatively low accuracy and robustness, tend to fail in certain challenging environments. In order to design a bionic SLAM system with a novel framework and relatively high practicality, and to facilitate the development of bionic SLAM research, in this paper we present LFVB-BioSLAM, a bionic SLAM system with a light-weight LiDAR-based front end and a bio-inspired vision-based back end. We adopt a range flow-based LiDAR odometry as the front end of the SLAM system, providing the odometry estimation for the back end, and we propose a biologically-inspired back end processing algorithm based on the monocular RGB camera, performing loop closure detection and path integration. Our method is verified through real-world experiments, and the results show that LFVB-BioSLAM outperforms RatSLAM, a vision-based bionic SLAM algorithm, and RF2O, a laser-based horizontal planar odometry algorithm, in terms of accuracy and robustness.

The colour‐enhanced point cloud map is increasingly being employed in fields such as robotics, 3D reconstruction and virtual reality. The authors propose ER‐Mapping (Extrinsic Robust coloured Mapping system using residual evaluation and selection). ER‐Mapping consists of two components: the simultaneous localisation and mapping (SLAM) subsystem and the colouring subsystem. The SLAM subsystem reconstructs the geometric structure, where it employs a dynamic threshold‐based residual selection in LiDAR‐inertial odometry to improve mapping accuracy. On the other hand, the colouring subsystem focuses on recovering texture information from input images and innovatively utilises 3D–2D feature selection and optimisation methods, eliminating the need for strict hardware time synchronisation and highly accurate extrinsic parameters. Experiments were conducted in both indoor and outdoor environments. The results demonstrate that our system can enhance accuracy, reduce computational costs and achieve extrinsic robustness.

This paper introduces the design and implementation of MS-Mapper, a LiDAR mapping system incorporating a mechanical structure based on a step motor and steering gear. The hardware configuration of MS-Mapper allows for adjustable vertical and horizontal orientations of the LiDAR sensor. The MS-Mapper has two configurations during the mapping process: stationary mapping (S-mapping), which is dedicated to generating detailed point cloud maps and providing pose drift correction. mobile mapping (M-mapping), which is utilized for completing map cavities and ensuring comprehensive coverage mapping. This dual-configuration approach employed by MS-Mapper significantly enhances pose estimation accuracy, particularly in challenging environments such as lengthy corridors. To further improve pose estimation accuracy, we propose a residual selection-based bundle adjustment algorithm. Additionally, we propose a dynamic Gaussian Mixture Model (GMM) tree map representation, which is designed to accommodate dynamic growth. This representation effectively reduces the number of redundant Gaussian models, and minimizes data association costs compared to utilizing a dense point cloud. Furthermore, a configuration selection algorithm is proposed to detect pose estimation degeneration and determine optimal opportunities for switching configurations. In summary, the MS-Mapper system amalgamates the strengths of traditional Terrestrial Laser Scanner (TLS) and LiDAR SLAM approaches. MS-Mapper provides a robust solution for achieving accurate mapping results.