Curvefusion-A Method for Combining Estimated Trajectories with Applications to SLAM and Time-Calibration.

Mapping and localization of mobile robots in an unknown environment are essential for most high-level operations like autonomous navigation or exploration. This paper presents a novel approach for combining estimated trajectories, namely curvefusion. The robot used in the experiments is equipped with a horizontally mounted 2D profiler, a constantly spinning 3D laser scanner and a GPS module. The proposed algorithm first combines trajectories from different sensors to optimize poses of the planar three degrees of freedom (DoF) trajectory, which is then fed into continuous-time simultaneous localization and mapping (SLAM) to further improve the trajectory. While state-of-the-art multi-sensor fusion methods mainly focus on probabilistic methods, our approach instead adopts a deformation-based method to optimize poses. To this end, a similarity metric for curved shapes is introduced into the robotics community to fuse the estimated trajectories. Additionally, a shape-based point correspondence estimation method is applied to the multi-sensor time calibration. Experiments show that the proposed fusion method can achieve relatively better accuracy, even if the error of the trajectory before fusion is large, which demonstrates that our method can still maintain a certain degree of accuracy in an environment where typical pose estimation methods have poor performance. In addition, the proposed time-calibration method also achieves high accuracy in estimating point correspondences.

The Actor-Dueling-Critic Method for Reinforcement Learning.

Model-free reinforcement learning is a powerful and efficient machine-learning paradigm which has been generally used in the robotic control domain. In the reinforcement learning setting, the value function method learns policies by maximizing the state-action value (Q value), but it suffers from inaccurate Q estimation and results in poor performance in a stochastic environment. To mitigate this issue, we present an approach based on the actor-critic framework, and in the critic branch we modify the manner of estimating Q-value by introducing the advantage function, such as dueling network, which can estimate the action-advantage value. The action-advantage value is independent of state and environment noise, we use it as a fine-tuning factor to the estimated Q value. We refer to this approach as the actor-dueling-critic (ADC) network since the frame is inspired by the dueling network. Furthermore, we redesign the dueling network part in the critic branch to make it adapt to the continuous action space. The method was tested on gym classic control environments and an obstacle avoidance environment, and we design a noise environment to test the training stability. The results indicate the ADC approach is more stable and converges faster than the DDPG method in noise environments.

Integrate Point-Cloud Segmentation with 3D LiDAR Scan-Matching for Mobile Robot Localization and Mapping.

Localization and mapping are key requirements for autonomous mobile systems to perform navigation and interaction tasks. Iterative Closest Point (ICP) is widely applied for LiDAR scan-matching in the robotic community. In addition, the standard ICP algorithm only considers geometric information when iteratively searching for the nearest point. However, ICP individually cannot achieve accurate point-cloud registration performance in challenging environments such as dynamic environments and highways. Moreover, the computation of searching for the closest points is an expensive step in the ICP algorithm, which is limited to meet real-time requirements, especially when dealing with large-scale point-cloud data. In this paper, we propose a segment-based scan-matching framework for six degree-of-freedom pose estimation and mapping. The LiDAR generates a large number of ground points when scanning, but many of these points are useless and increase the burden of subsequent processing. To address this problem, we first apply an image-based ground-point extraction method to filter out noise and ground points. The point cloud after removing the ground points is then segmented into disjoint sets. After this step, a standard point-to-point ICP is applied into to calculate the six degree-of-freedom transformation between consecutive scans. Furthermore, once closed loops are detected in the environment, a 6D graph-optimization algorithm for global relaxation (6D simultaneous localization and mapping (SLAM)) is employed. Experiments based on publicly available KITTI datasets show that our method requires less runtime while at the same time achieves higher pose estimation accuracy compared with the standard ICP method and its variants.

A Thermal Performance Analysis and Comparison of Fiber Coils with the D-CYL Winding and QAD Winding Methods.

The thermal performance under variable temperature conditions of fiber coils with double-cylinder (D-CYL) and quadrupolar (QAD) winding methods is comparatively analyzed. Simulation by the finite element method (FEM) is done to calculate the temperature distribution and the thermal-induced phase shift errors in the fiber coils. Simulation results reveal that D-CYL fiber coil itself has fragile performance when it experiences an axially asymmetrical temperature gradient. However, the axial fragility performance could be improved when the D-CYL coil meshes with a heat-off spool. Through further simulations we find that once the D-CYL coil is provided with an axially symmetrical temperature environment, the thermal performance of fiber coils with the D-CYL winding method is better than that with the QAD winding method under the same variable temperature conditions. This valuable discovery is verified by two experiments. The D-CYL winding method is thus promising to overcome the temperature fragility of interferometric fiber optic gyroscopes (IFOGs).