In this paper we deal with the camera pose estimation problem from a set of 2D/3D line correspondences, which is also known as PnL (Perspective-n-Line) problem. We carry out our study by comparing PnL with the well-studied PnP (Perspective-n-Point) problem, and our contributions are three-fold: (1) We provide a complete 3D configuration analysis for P3L, which includes the wellknown P3P problem as well as several existing analyses as special cases. (2) By exploring the similarity between PnL and PnP, we propose a new subset-based PnL approach as well as a series of linear-formulation-based PnL approaches inspired by their PnP counterparts. (3) The proposed linear-formulation-based methods can be easily extended to deal with the line and point features simultaneously.

Animals, such as Savannah sparrows and North American monarch butterflies, are able to obtain compass information from skylight polarization patterns to help them navigate effectively and robustly. Inspired by excellent navigation ability of animals, this paper proposes a novel image-based polarized light compass, which has the advantages of having a small size and being light weight. Firstly, the polarized light compass, which is composed of a Charge Coupled Device (CCD) camera, a pixelated polarizer array and a wide-angle lens, is introduced. Secondly, the measurement method of a skylight polarization pattern and the orientation method based on a single scattering Rayleigh model are presented. Thirdly, the error model of the sensor, mainly including the response error of CCD pixels and the installation error of the pixelated polarizer, is established. A calibration method based on iterative least squares estimation is proposed. In the outdoor environment, the skylight polarization pattern can be measured in real time by our sensor. The orientation accuracy of the sensor increases with the decrease of the solar elevation angle, and the standard deviation of orientation error is $0.{15}^{\circ }$ at sunset. Results of outdoor experiments show that the proposed polarization navigation sensor can be used for outdoor autonomous navigation.

Visual navigation and three-dimensional (3D) scene reconstruction are essential for robotics to interact with the surrounding environment. Large-scale scenarios and computational robustness are great challenges facing the research community to achieve this goal. This paper raises a pose-only imaging geometry representation and algorithms that might help solve these challenges. The pose-only representation, equivalent to the classical multiple-view geometry, is discovered to be linearly related to camera global translations, which allows for efficient and robust camera motion estimation. As a result, the spatial feature coordinates can be analytically reconstructed and do not require nonlinear optimization. Comprehensive experiments demonstrate that the computational efficiency of recovering the scene and associated camera poses is significantly improved by 2-4 orders of magnitude.

Optimization-based VI-SLAM focuses on the establishment of the loss function using both inertial and visual constraints. Preintegration theory is commonly used to express inertial constraints, but it lacks the merging equation between keyframes, challenging VI-SLAM from culling and merging redundant keyframes. To address this, we establish an on-manifold preintegration merging theory, including the merging of preintegrated terms, noise covariance, and Jacobians for bias updating, which significantly improves the preintegration theory and provides theoretical support for the keyframe management function of VI-SLAM. Visual constraints are typically expressed using multiple view geometry with three-dimensional (3D) points optimized as scene structure parameters. However, the excessive dimensionality of the optimization parameters generated by 3D points can lead to computational bottlenecks. Through the recent pose-only imaging geometry representation, we construct a lightweight optimization algorithm for SLAM that avoids the dimensional explosion in bundle adjustment (BA). Based on the above, we propose a 3D points-free SLAM optimizer. The proposed algorithms are validated on simulation, public datasets, and real-world experiments, and compared against advanced open-source systems such as ORB-SLAM3 and VINS.

In the challenging environment, a reliable and autonomous navigation is the key performance factor for robotics. This paper presents a bio-inspired multi-sensor navigation system with the skylight compass and visual place recognition for the UAVs in the GNSS-denied environment. The system can integrate the position and heading constraints outputs from a skylight polarized sensor, a micro-inertia sensor and a monocular camera. An optimal orientation algorithm is proposed with the skylight polarized patterns for obtaining the heading constraint. We also propose a two-dimensional visual place recognition for providing the position constraint in the aerial environment and the metric encoding pattern and place recognition are presented in detail. Based on the position and heading constraints, a biologically multi-sensor integrating system with Kalman Filter has been developed to accurately estimate the motion information for UAVs. Unlike previous approaches having a fragile navigation scheme and path-error accumulation, our system can build a bionic and robust association between the motion metric information and the sensor inputs, and use reliable position and heading constraints for improving the navigation performance. The flying experiments have demonstrated that the proposed bio-inspired navigation system can outperform other vision-based navigation algorithms and the position errors are convergent and do not grow with the flying distance, in which the best performance of position precision is within 5m. Finally, we present interesting insights gained with respect to further work in robotics.

The development of object detection in infrared images has attracted more attention in recent years. However, there are few studies on multi-scale object detection in infrared street scene images. Additionally, the lack of high-quality infrared datasets hinders research into such algorithms. In order to solve these issues, we firstly make a series of modifications based on Faster Region-Convolutional Neural Network (R-CNN). In this paper, a double-layer region proposal network (RPN) is proposed to predict proposals of different scales on both fine and coarse feature maps. Secondly, a multi-scale pooling module is introduced into the backbone of the network to explore the response of objects on different scales. Furthermore, the inception4 module and the position sensitive region of interest (ROI) align (PSalign) pooling layer are utilized to explore richer features of the objects. Thirdly, this paper proposes instance level data augmentation, which takes into account the imbalance between categories while enlarging dataset. In the training stage, the online hard example mining method is utilized to further improve the robustness of the algorithm in complex environments. The experimental results show that, compared with baseline, our detection method has state-of-the-art performance.