Self-driving cars have experienced rapid development in the past few years, and Simultaneous Localization and Mapping (SLAM) is considered to be their basic capabilities. In this article, we propose a direct vision LiDAR fusion SLAM framework that consists of three modules. Firstly, a two-staged direct visual odometry module, which consists of a frame-to-frame tracking step, and an improved sliding window based thinning step, is proposed to estimate the accurate pose of the camera while maintaining efficiency. Secondly, every time a keyframe is generated, a dynamic objects considered LiDAR mapping module is utilized to refine the pose of the keyframe to obtain higher positioning accuracy and better robustness. Finally, a Parallel Global and Local Search Loop Closure Detection (PGLS-LCD) module that combines visual Bag of Words (BoW) and LiDAR-Iris feature is applied for place recognition to correct the accumulated drift and maintain a globally consistent map. We conducted a large number of experiments on the public dataset and our mobile robot dataset to verify the effectiveness of each module in our framework. Experimental results show that the proposed algorithm achieves more accurate pose estimation than the state-of-the-art methods.

The integration of the camera and LiDAR has played an important role in the field of autonomous driving, for example in visual–LiDAR SLAM and 3D environment fusion perception, which rely on precise geometrical extrinsic calibration. In this paper, we proposed a fully automatic end-to-end method based on the 3D–2D corresponding mask (CoMask) to directly estimate the extrinsic parameters with high precision. Simple subtraction was applied to extract the candidate point cluster from the complex background, and then 3D LiDAR points located on checkerboard were selected and refined by spatial growth clustering. Once the distance transform of 2D checkerboard mask was generated, the extrinsic calibration of the two sensors could be converted to 3D–2D mask correspondence alignment. A simple but efficient strategy combining the genetic algorithm with the Levenberg–Marquardt method was used to solve the optimization problem globally without any initial estimates. Both simulated and realistic experiments showed that the proposed method could obtain accurate results without manual intervention, special environment setups, or prior initial parameters. Compared with the state of the art, our method has obvious advantages in accuracy, robustness, and noise resistance. Our code is open-source on GitHub.

In the field of simultaneous localization and mapping (SLAM), map-based localization has been widely used in autonomous driving, particularly for all-speed and all-road adaptive cruise, automatic parking, and other high-level functions. As a result, LiDAR sensors are frequently used in visual-based SLAM to improve the overall accuracy of ego-motion estimation and environment reconstruction. In this paper, a novel tightly-coupled monocular Hybrid Visual LiDAR SLAM (HVL-SLAM), which utilizes both visual and LiDAR measurements in tracking and mapping. Firstly, the proposed method reduces the 3D uncertainty of features by employing object segmentation and Delaunay triangulation. The motion between adjacent frames is then estimated using a hybrid tracking module that minimizes photometric and reprojection error. Finally, a joint optimization method for refining the pose is proposed, which incorporates visual and LiDAR measurements into optimization with dynamic weights, resulting in higher positioning accuracy and robustness. The experiments on the public KITTI odometry benchmark and real-world outdoor datasets demonstrate that HVL-SLAM outperforms state-of-the-art approaches in terms of pose estimation and mapping performance. The code is released to the community ¹ .

Purpose: The purpose of this paper is specifically to provide a more intelligent locomotion planning method for a hexapod robot based on trajectory optimization, which could reduce the complexity of locomotion design, shorten time of design and generate efficient and accurate motion. Design/methodology/approach: The authors generated locomotion for the hexapod robot based on trajectory optimization method and it just need to specify the high-level motion requirements. Here the authors first transcribed the trajectory optimization problem to a nonlinear programming problem, in which the specified motion requirements and the dynamics with complementarity constraints were defined as the constraints, then a nonlinear solver was used to solve. The leg compliance was taken into consideration and the generated motions were deployed on the hexapod robot prototype to prove the utility of the method and, meanwhile, the influence of different environments was considered. Findings: The generated motions were deployed on the hexapod robot and the movements were demonstrated very much in line with the planning. The new planning method does not require lots of parameter-tuning work and therefore significantly reduces the cycle for designing a new locomotion. Originality/value: A locomotion generation method based on trajectory optimization was constructed for a 12-degree of freedom hexapod robot. The variable stiffness compliance of legs was considered to improve the accuracy of locomotion generation. And also, different from some simulation work before, the authors have designed the locomotion in three cases and constructed field tests to demonstrate its utility.

Self-supervised monocular depth estimation from oblique UAV videos is crucial for enabling autonomous navigation and large-scale mapping. However, existing self-supervised monocular depth estimation methods face key challenges in UAV oblique video scenarios: depth discontinuity from geometric distortion under complex viewing angles, and spatial ambiguity in weakly textured regions. These challenges highlight the need for models that combine global reasoning with geometric awareness. Accordingly, we propose RMTDepth, a self-supervised monocular depth estimation framework for UAV imagery. RMTDepth integrates an enhanced Retentive Vision Transformer (RMT) backbone, introducing explicit spatial priors via a Manhattan distance-driven spatial decay matrix for efficient long-range geometric modeling, and embeds a neural window fully-connected CRF (NeW CRFs) module in the decoder to refine depth edges by optimizing pairwise relationships within local windows. To mitigate noise in COLMAP-generated depth for real-world UAV datasets, we constructed a high-fidelity UE4/AirSim simulation environment, which generated a large-scale precise depth dataset (UAV SIM Dataset) to validate robustness. Comprehensive experiments against seven state-of-the-art methods across UAVID Germany, UAVID China, and UAV SIM datasets demonstrate that our model achieves SOTA performance in most scenarios.