A reliable and accurate visual object 6DoF pose tracking system for texture-less objects plays an important role in various fields of modern industry hence it has been a hot research topic for decades. Traditional feature-based pose tracking methods require rich feature points on objects, and it cannot handle texture-less objects. To tackle this problem, the paper proposes a novel edge-based method for continuous 6DOF pose tracking of texture-less objects. The pose of the object is estimated by minimizing the matching error between extracted image edges and re-projected CAD model edges. The matching error is represented using the Directional Chamfer Matching (DCM) Tensor. Compared with previous methods, the proposed system improves the overall performance of pose tracking system in two ways. Firstly, the method proposes an analytical mathematic model in the optimization process; Secondly, the method proposes an adaptive edge point weighting algorithm to tackle the occlusion or edge weakness problem. Both methods help improve the accuracy and robustness of the pose estimation system. With the benefit of GPU acceleration on DCM Tensor calculation the proposed method could run in real time. Extensive experiments are conducted both on public datasets and CG-rendered synthetic datasets to validate the accuracy and robustness of the proposed algorithm against other state-of-the-art methods.

Simultaneous Location and Mapping (SLAM) is a common algorithm for position estimation in GNSS-denied environments. However, the high structural consistency and low lighting conditions in tunnel environments pose challenges for traditional visual SLAM and LiDAR SLAM. To this end, this paper presents LiDAR and optical flow fusion odometry (LOFF), which uses a direction-separated data fusion method to fuse optical flow odometry into the degenerate direction of the LiDAR SLAM without sacrificing the accuracy. Moreover, LOFF incorporates detectors and a compensator, allowing for a smooth transition between general environments and degeneracy environments. This capability facilitates the stable flight of unmanned aerial vehicles (UAVs) in GNSS-denied tunnel environments, including corners and long-distance consistency. Through real-world experiments conducted in a GNSS-denied pedestrian tunnel, we demonstrate the superior position accuracy and trajectory smoothness of LOFF compared to state-of-the-art visual SLAM and LiDAR SLAM.

Unmanned aerial vehicle (UAV) navigation systems face significant challenges in complex environments, such as sensor degradation and signal loss. This study proposes the NSDDI-AFF (Normalized Single-Dimensional Degradation Isolation with Adaptive Federated Filtering) method to address these issues. By integrating adaptive thresholding, degradation isolation, and multi-sensor fusion, the method dynamically identifies degraded channels and ensures robust state estimation. Evaluated in a semi-enclosed coal yard and in a hydropower pipeline, NSDDI-AFF reduced positional errors by 97.5% and 95.7% compared to LIO-SAM and Fast-LIO2, achieving a position RMSE of 0.05 m and orientation RMSE of 0.5°. The method detected degradation early (120 s) and maintained mapping accuracy with geometric errors below 0.5%. These results demonstrate that NSDDI-AFF significantly enhances UAV positioning accuracy, fault tolerance, and mapping reliability, making it a robust solution for challenging industrial applications.

Accurate velocity measurement of unmanned aerial vehicles (UAVs) is essential in various applications. Traditional vision-based methods rely heavily on visual features, which are often inadequate in low-light or feature-sparse environments. This study presents a novel approach to measure the axial velocity of UAVs using motion blur images captured by a UAV-mounted monocular camera. We introduce a motion blur model that synthesizes imaging from neighboring frames to enhance motion blur visibility. The synthesized blur frames are transformed into spectrograms using the Fast Fourier Transform (FFT) technique. We then apply a binarization process and the Radon transform to extract light-dark stripe spacing, which represents the motion blur length. This length is used to establish a model correlating motion blur with axial velocity, allowing precise velocity calculation. Field tests in a hydropower station penstock demonstrated an average velocity error of 0.048 m/s compared to ultra-wideband (UWB) measurements. The root-mean-square error was 0.025, with an average computational time of 42.3 ms and CPU load of 17%. These results confirm the stability and accuracy of our velocity estimation algorithm in challenging environments.

This study proposes a novel method for estimating the axial velocity of unmanned aerial vehicles (UAVs) using motion blur images captured in environments where GPS signals are unavailable and lighting conditions are poor, such as underground tunnels and corridors. By correlating the length of motion blur observed in images with the UAV’s axial speed, the method addresses the limitations of traditional techniques in these challenging scenarios. We enhanced the accuracy by synthesizing motion blur images from neighboring frames, which is particularly effective at low speeds where single-frame blur is minimal. Six flight experiments conducted in the corridor of a hydropower station demonstrated the effectiveness of our approach, achieving a mean velocity error of 0.065 m/s compared to ultra-wideband (UWB) measurements and a root-mean-squared error within 0.3 m/s. The results highlight the stability and precision of the proposed velocity estimation algorithm in confined and low-light environments.