Global localization in 3D point clouds is a challenging problem of estimating the pose of vehicles without any prior knowledge. In this paper, a solution to this problem is presented by achieving place recognition and metric pose estimation in the global prior map. Specifically, we present a semi-handcrafted representation learning method for LiDAR point clouds using siamese LocNets, which states the place recognition problem to a similarity modeling problem. With the final learned representations by LocNet, a global localization framework with range-only observations is proposed. To demonstrate the performance and effectiveness of our global localization system, KITTI dataset is employed for comparison with other algorithms, and also on our long-time multi-session datasets for evaluation. The result shows that our system can achieve high accuracy.
For frame level descriptors, there were some works utilizing the handcrafted representation. @cite_7 proposed an appearance based loop detection method using NDT surface representation. R "o @cite_12 proposed a 1-D histogram to describe the range distribution of the entire point cloud. For learning based method, Granstr "o m and Sch "o n @cite_8 used features that capture important geometric and statistical properties of 2D point clouds. The features are used as input to the machine learning algorithm - Adaboosts to build a classifier for matching. Based on the deep learning, our method set to develop the learning based representations for 3D point clouds.
@cite_12 @cite_7 @cite_8
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In this paper we address the loop closure detection problem in simultaneous localization and mapping ( slam ), and present a method for solving the problem using pairwise comparison of point clouds in both two and three dimensions. The point clouds are mathematically described using features that capture important geometric and statistical properties. The features are used as input to the machine learning algorithm AdaBoost, which is used to build a non-linear classifier capable of detecting loop closure from pairs of point clouds. Vantage point dependency in the detection process is eliminated by only using rotation invariant features, thus loop closure can be detected from an arbitrary direction. The classifier is evaluated using publicly available data, and is shown to generalize well between environments. Detection rates of 66 , 63 and 53 for 0 false alarm rate are achieved for 2D outdoor data, 3D outdoor data and 3D indoor data, respectively. In both two and three dimensions, experiments are performed using publicly available data, showing that the proposed algorithm compares favourably with related work. In this paper, we propose the 3DFeat-Net which learns both 3D feature detector and descriptor for point cloud matching using weak supervision. Unlike many existing works, we do not require manual annotation of matching point clusters. Instead, we leverage on alignment and attention mechanisms to learn feature correspondences from GPS INS tagged 3D point clouds without explicitly specifying them. We create training and benchmark outdoor Lidar datasets, and experiments show that 3DFeat-Net obtains state-of-the-art performance on these gravity-aligned datasets. This work addresses the problem of real-time 3D shape based object class recognition, its scaling to many categories and the reliable perception of categories. A novel shape descriptor for partial point clouds based on shape functions is presented, capable of training on synthetic data and classifying objects from a depth sensor in a single partial view in a fast and robust manner. The classification task is stated as a 3D retrieval task finding the nearest neighbors from synthetically generated views of CAD-models to the sensed point cloud with a Kinect-style depth sensor. The presented shape descriptor shows that the combination of angle, point-distance and area shape functions gives a significant boost in recognition rate against the baseline descriptor and outperforms the state-of-the-art descriptors in our experimental evaluation on a publicly available dataset of real-world objects in table scene contexts with up to 200 categories. We propose a new approach to appearance-based loop detection for mobile robots, using three-dimensional (3D) laser scans. Loop detection is an important problem in the simultaneous localization and mapping (SLAM) domain, and, because it can be seen as the problem of recognizing previously visited places, it is an example of the data association problem. Without a flat-floor assumption, two-dimensional laser-based approaches are bound to fail in many cases. Two of the problems with 3D approaches that we address in this paper are how to handle the greatly increased amount of data and how to efficiently obtain invariance to 3D rotations. We present a compact representation of 3D point clouds that is still discriminative enough to detect loop closures without false positives (i.e., detecting loop closure where there is none). A low false-positive rate is very important because wrong data association could have disastrous consequences in a SLAM algorithm. Our approach uses only the appearance of 3D point clouds to detect loops and requires no pose information. We exploit the normal distributions transform surface representation to create feature histograms based on surface orientation and smoothness. The surface shape histograms compress the input data by two to three orders of magnitude. Because of the high compression rate, the histograms can be matched efficiently to compare the appearance of two scans. Rotation invariance is achieved by aligning scans with respect to dominant surface orientations. We also propose to use expectation maximization to fit a gamma mixture model to the output similarity measures in order to automatically determine the threshold that separates scans at loop closures from nonoverlapping ones. We discuss the problem of determining ground truth in the context of loop detection and the difficulties in comparing the results of the few available methods based on range information. Furthermore, we present quantitative performance evaluations using three real-world data sets, one of which is highly self-similar, showing that the proposed method achieves high recall rates (percentage of correctly identified loop closures) at low false-positive rates in environments with different characteristics. © 2009 Wiley Periodicals, Inc.