The use of 2D laser scanners is attractive for the autonomous driving industry because of its accuracy, light-weight and low-cost. However, since only a 2D slice of the surrounding environment is detected at each scan, it is a challenge to execute important tasks such as the localization of the vehicle. In this paper we present a novel framework that explores the use of deep Recurrent Convolutional Neural Networks (RCNN) for odometry estimation using only 2D laser scanners. The application of RCNNs provides the tools to not only extract the features of the laser scanner data using Convolutional Neural Networks (CNNs), but in addition it models the possible connections among consecutive scans using the Long Short-Term Memory (LSTM) Recurrent Neural Network. Results on a real road dataset show that the method can run in real-time without using GPU acceleration and have competitive performance compared to other methods, being an interesting approach that could complement traditional localization systems.
@cite_15: The authors describe a general-purpose, representation-independent method for the accurate and computationally efficient registration of 3-D shapes including free-form curves and surfaces. The method handles the full six degrees of freedom and is based on the iterative closest point (ICP) algorithm, which requires only a procedure to find the closest point on a geometric entity to a given point. The ICP algorithm always converges monotonically to the nearest local minimum of a mean-square distance metric, and the rate of convergence is rapid during the first few iterations. Therefore, given an adequate set of initial rotations and translations for a particular class of objects with a certain level of 'shape complexity', one can globally minimize the mean-square distance metric over all six degrees of freedom by testing each initial registration. One important application of this method is to register sensed data from unfixtured rigid objects with an ideal geometric model, prior to shape inspection. Experimental results show the capabilities of the registration algorithm on point sets, curves, and surfaces. >
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@cite_22: The Simultaneous Localization And Mapping (SLAM) problem has been well studied in the robotics community, especially using mono, stereo cameras or depth sensors. 3D depth sensors, such as Velodyne LiDAR, have proved in the last 10 years to be very useful to perceive the environment in autonomous driving, but few methods exist that directly use these 3D data for odometry. We present a new low-drift SLAM algorithm based only on 3D LiDAR data. Our method relies on a scan-to-model matching framework. We first have a specific sampling strategy based on the LiDAR scans. We then define our model as the previous localized LiDAR sweeps and use the Implicit Moving Least Squares (IMLS) surface representation. We show experiments with the Velodyne HDL32 with only 0.40 drift over a 4 km acquisition without any loop closure (i.e., 16 m drift after 4 km). We tested our solution on the KITTI benchmark with a Velodyne HDL64 and ranked among the best methods (against mono, stereo and LiDAR methods) with a global drift of only 0.69 .
@cite_7: The self-localization of a vehicle is still an ongoing and challenging task for the autonomous driving development. At the same time, the correct understanding of the vehicle's surroundings and the creation of a map with the traversed trajectory is essential for driving in complex urban scenarios. This paper proposes a solution to create an enriched global map of the environment while localizing the vehicle within it. We use the Evidential framework based on the Dempster-Shafer theory to create a map able to distinguish between static and dynamic obstacles and to keep the information from the entire traversed path. Additionally, we propose a method that at each current map creation estimates the vehicle's position by a grid matching algorithm based on image registration techniques. The method transforms the grid maps into images using the evidential data to extract relevant information to perform a better localization. The proposed method was tested on the KITTI dataset and shows that the solution is able to reduce the drift and improve the localization compared to other methods using similar configurations.
The use of laser scanners is also popular for pose estimation in robotic systems. The most classic method consists in matching two point clouds to estimate the transformation between them by minimizing the matching error, this solution is known as Iterative Closest Point (ICP) @cite_15 . More complex and robust methods to achieve the same goal have been proposed since then, like LOAM @cite_10 , which runs two different algorithms in parallel to achieve real time processing. One algorithm can run faster to obtain a low fidelity odometry, and another one slower for a more precise matching. One of the main challenges for this type of localization methods is the error caused by moving obstacles or lack of information because of occlusions. For this reason, some localization solutions @cite_7 @cite_22 are based on detecting what is static in the environment to only use this information for the scan matching process.