Simultaneous Localization and Mapping (SLAM) has been widely applied in various robotic missions, from rescue operations to autonomous driving. However, the generalization of SLAM algorithms remains a significant challenge, as current datasets often lack scalability in terms of platforms and environments. To address this limitation, we present FusionPortableV2, a multi-sensor SLAM dataset featuring sensor diversity, varied motion patterns, and a wide range of environmental scenarios. Our dataset comprises 27 sequences, spanning over 2.5 hours and collected from four distinct platforms: a handheld suite, a legged robot, an unmanned ground vehicle (UGV), and a vehicle. These sequences cover diverse settings, including buildings, campuses, and urban areas, with a total length of 38.7 km. Additionally, the dataset includes ground truth (GT) trajectories and RGB point cloud maps covering approximately 0.3 km ² . To validate the utility of our dataset in advancing SLAM research, we assess several state-of-the-art (SOTA) SLAM algorithms. Furthermore, we demonstrate the dataset’s broad application beyond traditional SLAM tasks by investigating its potential for monocular depth estimation. The complete dataset, including sensor data, GT, and calibration details, is accessible at https://fusionportable.github.io/dataset/fusionportable_v2 .

Hand-eye calibration (HEC) is a kernel technique guaranteeing precision industrial visual servoing and robotic grasping. Extensive studies have been conducted to various closed-form and iterative solutions to HEC problems using different pose parameterizations. However, these approaches are either sensitive to input noise or time-consuming for implementation. This paper provides a new perspective on a deterministic solution to two major branches of HEC problems of forms AX = XB and AX = YB. We use symbolic methods to derive a globally optimal solution. Different from representatives based on optimization, this method is not only the most accurate against others but also with repeatability of 100%. Experiments via industrial robotic manipulator verify the superiority of the proposed algorithm.

Localization, or position fixing, is an important problem in robotics research. In this paper, we propose a novel approach for long-term localization in a changing environment using 3D LiDAR. We first create the map of a real environment using GPS and LiDAR. Then, we divide the map into several small parts as the targets for cloud registration, which can not only improve the robustness but also reduce the registration time. We proposed a localization method called PointLocalization. PointLocalization allows us to fuse different kinds of odometers, which can optimize the accuracy and frequency of localization results. We evaluate our algorithm on an unmanned ground vehicle (UGV) using LiDAR and a wheel encoder, and obtain the localization results at more than 20 Hz after fusion. The algorithm can also localize the UGV in a 180-degree field of view (FOV). Using an outdated map captured six months ago, this algorithm shows great robustness, and the test results show that it can achieve an accuracy of 10 cm. PointLocalization has been tested for a period of more than six months in a crowded factory and has operated successfully over a distance of more than 2000 km.

The stability of visual odometry (VO) systems is undermined by degraded image quality, especially in environments with significant illumination changes. This study employs a deep reinforcement learning (DRL) framework to train agents for exposure control, aiming to enhance imaging performance in challenging conditions. A lightweight image simulator is developed to facilitate the training process, enabling the diversification of image exposure and sequence trajectory. This setup enables completely offline training, eliminating the need for direct interaction with camera hardware and the real environments. Different levels of reward functions are crafted to enhance the VO systems, equipping the DRL agents with varying intelligence. Extensive experiments have shown that our exposure control agents achieve superior efficiency—with an average inference duration of 1.58 ms per frame on a CPU—and respond more quickly than traditional feedback control schemes. By choosing an appropriate reward function, agents acquire an intelligent understanding of motion trends and can anticipate future changes in illumination. This predictive capability allows VO systems to deliver more stable and precise odometry results.

Low-cost Inertial Measurement Units (IMUs) are ubiquitously used in the attitude estimation of cell phones and robots. Accurate and robust IMU calibration is required to ensure attitude estimation accuracy. This paper proposes an accurate and robust equipment-free IMU calibration method. We do not assume gyroscope biases are invariant during calibration compared with conventional methods because low-cost IMUs like MPU6000 have large gyroscope biases instability. We propose introducing biases removal and outlier-aware optimization to alleviate the impact of variant gyroscope biases. Furthermore, we introduce a multi-resolution analysis based static detector to detect subtle IMU motion in real data collection. Our detector can detect 84% subtle motions (1-degree rotations) present in the simulated calibration data while the conventional variance thresholding detector can only detect 31% of them. In addition, we derive a proper data collection method to guide the user to collect data effectively. We benchmark our method with another existing equipment-free method with synthetic and real datasets. The results in synthetic datasets show that our method is 25% more accurate and robust than the existing method. The results in real datasets vouch that our method achieves an estimation of IMU intrinsic parameters comparable to the ground truth. Furthermore, the roll and pitch estimation of MPU6000 using our calibration method are close to (< 0.15° in ±30°) that of an expensive factory-calibrated IMU in real testing.

State estimation encounters significant hurdles in scale ambiguity, both when assimilating data from scale-uninformed sources such as Structure from Motion (SfM) and when handling normalized point clouds, each scenario demanding robust solutions to achieve consistent scale and accurate estimation. Addressing this critical issue, we propose the Scaled Quadratic Pose Estimation Problem (sQPEP), a novel unified framework designed to enhance scale estimation in various state estimation algorithms. Our framework not only establishes a globally optimal solution strategy for the precise estimation of pose and scale factors but also systematically categorizes a broad spectrum of pose estimation challenges. This is crucial for advancing our theoretical understanding and the practical application of these solutions. The sQPEP framework consolidates a range of scale and pose estimation challenges into a unified theoretical paradigm, thereby refining the methodology for these estimations. By applying algebraic techniques, we have effectively bifurcated the problem into two distinct categories. Subsequently, we have deduced globally optimal solutions and unveiled two robust solvers. These solvers are proficient in generating 80 and 81 solutions for their respective problem classes, featuring elimination template dimensions of 664×744 and 521×602. Our method’s efficacy has been rigorously confirmed through experimental validation, which demonstrates its consistent performance in degenerate conditions and its superior noise immunity. These results bolster the framework’s applicability to intricate scenarios encountered in real-world settings.