This paper presents an integrated GNSS/LiDAR- SLAM pose estimation framework to perform large-scale 3D map building in partially GNSS-denied outdoor environments. The framework takes advantage of the complementarity between GNSS positioning and LiDAR-SLAM to decompose the map building task according to the GNSS RTK status. When mapping in GNSS-denied scenes, a 3D LiDAR-SLAM algorithm is adopted to estimate poses and a correction algorithm is presented to correct drift errors. On the other hand, when mapping in open scenes, a GNSS-initialized LiDAR mapping algorithm (GL-mapping) is proposed to loosely couple GNSS positioning and LiDAR data registration. It can perform the orientation estimation without the use of either the high-cost inertial sensing device or the GNSS dual-antenna. Experiments are conducted in large-scale outdoor environments to demonstrate that the proposed framework can accomplish simultaneous pose estimation and map building with high precision in both open scenes and GNSS-denied scenes.

The recent success of deep learning in 3-D data analysis relies upon the availability of large annotated data sets. However, creating 3-D data sets with point-level labels are extremely challenging and require a huge amount of human efforts. This paper presents a novel open-sourced method to extract light detection and ranging point clouds with ground truth annotations from a simulator automatically. The virtual sensor can be configured to simulate various real devices, from 2-D laser scanners to 3-D real-time sensors. Experiments are conducted to show that using additional synthetic data for training can: 1) achieve a visible performance boost in accuracy; 2) reduce the amount of manually labeled real-world data; and 3) help to improve the generalization performance across data sets.

Multi-sensor fusion plays a key role in 2D laser based robot location and navigation. Although it has achieved great success, there are still some challenges, e.g., being fragile when having large angular rotation. In this paper, we present a deep learning based approach to localizing a mobile robot using a 2D laser and an Inertial Measurement Unit. A novel Recurrent Convolutional Neural Network (RCNN) based architecture is developed to fuse laser and inertial data for scan-to-scan pose estimation. A scan-to-submap optimization is also introduced to optimize the poses estimated by the RCNN for enhanced robustness and accuracy. Extensive experiments have been conducted in both simulation and practice with a real mobile robot, verifying the effectiveness of the proposed deep sensor fusion system.

In this paper, we introduce a new research problem termed online feature transformation learning in the context of multiclass object category recognition. The learning of a feature transformation is viewed as learning a global similarity metric function in an online manner. We first consider the problem of online learning a feature transformation matrix expressed in the original feature space and propose an online passive aggressive feature transformation algorithm. Then these original features are mapped to kernel space and an online single kernel feature transformation (OSKFT) algorithm is developed to learn a nonlinear feature transformation. Based on the OSKFT and the existing Hedge algorithm, a novel online multiple kernel feature transformation algorithm is also proposed, which can further improve the performance of online feature transformation learning in large-scale application. The classifier is trained with k nearest neighbor algorithm together with the learned similarity metric function. Finally, we experimentally examined the effect of setting different parameter values in the proposed algorithms and evaluate the model performance on several multiclass object recognition data sets. The experimental results demonstrate the validity and good performance of our methods on cross-domain and multiclass object recognition application.

A challenging problem in object recognition is to train a robust classifier with small and imbalanced data set. In such cases, the learned classifier tends to overfit the training data and has low prediction accuracy on the minority class. In this paper, we address the problem of class imbalanced object recognition by combining synthetic minorities over-sampling technique (SMOTE) and instance-based transfer boosting to rebalance the skewed class distribution. We present ways of generating synthetic instances under the learning framework of transfer Adaboost. A novel weighted SMOTE technique (WSMOTE) is proposed to generate weighted synthetic instances with weighted source and target instances at each boosting round. Based on WSMOTE, we propose a novel class imbalanced transfer boosting algorithm called WSMOTE-TrAdaboost and experimentally demonstrate its effectiveness on four datasets (Office, Caltech256, SUN2012, and VOC2012) for object recognition application. Bag-of-words model with SURF features and histogram of oriented gradient features are separately used to represent an image. We experimentally demonstrated the effectiveness and robustness of our approaches by comparing it with several baseline algorithms in boosting family for class imbalanced learning.

In this article, a novel 3-D lidar-assisted monocular visual simultaneous localization and mapping (LAMV-SLAM) framework is proposed for mobile robots in outdoor environments. LAMV-SLAM can run in real time without a GPU and build a dense map with real scale. An online photometric calibration thread is integrated into LAMV-SLAM to eliminate the photometric disturbances in images. The tracking thread combines the lidar and vision data to estimate and refine the frame-to-frame transformation. In this thread, the depth fusion algorithm is proposed to provide accurate depth values for the extracted visual features by combining the lidar points, and a novel two-stage optimization method is proposed to utilize the fused lidar–vision data to estimate the camera transformation with the real scale. A parallel mapping thread generates new map points based on depth filter and lidar–vision data fusion. A loop closing thread further reduces the accumulative errors of the system. To verify the accuracy and efficiency of the system, we evaluated the proposed pipeline on the KITTI odometry benchmark, and our LAMV-SLAM achieves a 0.81% of relative position drift while running at over $3\times $ real-time speed. To verify the robustness of the system in challenging environments, experiments were carried out on the North Campus Long-Term (NCLT) and NuScenes datasets. Moreover, real-world experiments were applied to our mobile robot platform to show the practicability and validity of the proposed approach.