Abstract
Abstract. Having a good estimate of the position and orientation of a mobile agent is essential for many application domains such as robotics, autonomous driving, and virtual and augmented reality. In particular, when using LiDAR and IMU sensors as the inputs, most existing methods still use classical filter-based fusion methods to achieve this task. In this work, we propose DeepLIO, a modular, end-to-end learning-based fusion framework for odometry estimation using LiDAR and IMU sensors. For this task, our network learns an appropriate fusion function by considering different modalities of its input latent feature vectors. We also formulate a loss function, where we combine both global and local pose information over an input sequence to improve the accuracy of the network predictions. Furthermore, we design three sub-networks with different modules and architectures derived from DeepLIO to analyze the effect of each sensory input on the task of odometry estimation. Experiments on the benchmark dataset demonstrate that DeepLIO outperforms existing learning-based and model-based methods regarding orientation estimation and shows a marginal position accuracy difference.
Highlights
Odometry estimation – i.e. estimating the 3D position and orientation of an agent through time and space – using sensory information such as cameras, light detection and ranging sensors (LiDARs), and inertial measurements units (IMU) is a highly active field of research in computer vision with a wide range of application domains such as autonomous driving (Geiger et al, 2012), robotics (Cadena et al, 2016), building information modeling (BIM) (Roca et al, 2014) and virtual and augmented reality (Klein and Murray, 2007)
We introduce a cascade modular network architecture, that learns a robust fusion of LiDAR frames and IMU measurements for the task of relative pose estimation between a pair of consecutive LiDAR frames Ft−1 and Ft
By deploying the deep inertial odometry network (DeepIO) we aim to investigate the ability of different only IMU-based networks to extract the hidden information about the dynamic motion of the mobile agent from the IMU measurements to predict the current pose of the agent w.r.t. its previous pose
Summary
Odometry estimation – i.e. estimating the 3D position and orientation of an agent through time and space – using sensory information such as cameras, light detection and ranging sensors (LiDARs), and inertial measurements units (IMU) is a highly active field of research in computer vision with a wide range of application domains such as autonomous driving (Geiger et al, 2012), robotics (Cadena et al, 2016), building information modeling (BIM) (Roca et al, 2014) and virtual and augmented reality (Klein and Murray, 2007). The main issue of VO lies both in the scale drift due to scale ambiguity, especially when monocular cameras are used, and the unsoundness of the predictions in the lack of enough features in images. To tackle these problems many researchers start to fuse the VO predictions with other sensory information, like IMU (Nutzi et al, 2011, Chu et al, 2012) by applying e.g. Extended Kalman Filter (EKF). Most ICP algorithms suffer from 1) Wrong correspondences 2) Inaccurate initialization 3) Lacking of correct covariance matrix 4) High computational power
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