684 research outputs found
Observability analysis and optimal sensor placement in stereo radar odometry
© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Localization is the key perceptual process closing the loop of autonomous navigation, allowing self-driving vehicles to operate in a deliberate way. To ensure robust localization, autonomous vehicles have to implement redundant estimation processes, ideally independent in terms of the underlying physics behind sensing principles. This paper presents a stereo radar odometry system, which can be used as such a redundant system, complementary to other odometry estimation processes, providing robustness for long-term operability. The presented work is novel with respect to previously published methods in that it contains: (i) a detailed formulation of the Doppler error and its associated uncertainty; (ii) an observability analysis that gives the minimal conditions to infer a 2D twist from radar readings; and (iii) a numerical analysis for optimal vehicle sensor placement. Experimental results are also detailed that validate the theoretical insights.Peer ReviewedPostprint (author's final draft
3D LiDAR Aided GNSS NLOS Mitigation for Reliable GNSS-RTK Positioning in Urban Canyons
GNSS and LiDAR odometry are complementary as they provide absolute and
relative positioning, respectively. Their integration in a loosely-coupled
manner is straightforward but is challenged in urban canyons due to the GNSS
signal reflections. Recent proposed 3D LiDAR-aided (3DLA) GNSS methods employ
the point cloud map to identify the non-line-of-sight (NLOS) reception of GNSS
signals. This facilitates the GNSS receiver to obtain improved urban
positioning but not achieve a sub-meter level. GNSS real-time kinematics (RTK)
uses carrier phase measurements to obtain decimeter-level positioning. In urban
areas, the GNSS RTK is not only challenged by multipath and NLOS-affected
measurement but also suffers from signal blockage by the building. The latter
will impose a challenge in solving the ambiguity within the carrier phase
measurements. In the other words, the model observability of the ambiguity
resolution (AR) is greatly decreased. This paper proposes to generate virtual
satellite (VS) measurements using the selected LiDAR landmarks from the
accumulated 3D point cloud maps (PCM). These LiDAR-PCM-made VS measurements are
tightly-coupled with GNSS pseudorange and carrier phase measurements. Thus, the
VS measurements can provide complementary constraints, meaning providing
low-elevation-angle measurements in the across-street directions. The
implementation is done using factor graph optimization to solve an accurate
float solution of the ambiguity before it is fed into LAMBDA. The effectiveness
of the proposed method has been validated by the evaluation conducted on our
recently open-sourced challenging dataset, UrbanNav. The result shows the fix
rate of the proposed 3DLA GNSS RTK is about 30% while the conventional GNSS-RTK
only achieves about 14%. In addition, the proposed method achieves sub-meter
positioning accuracy in most of the data collected in challenging urban areas
Improved Ground-Based Monocular Visual Odometry Estimation using Inertially-Aided Convolutional Neural Networks
While Convolutional Neural Networks (CNNs) can estimate frame-to-frame (F2F) motion even with monocular images, additional inputs can improve Visual Odometry (VO) predictions. In this thesis, a FlowNetS-based [1] CNN architecture estimates VO using sequential images from the KITTI Odometry dataset [2]. For each of three output types (full six degrees of freedom (6-DoF), Cartesian translation, and transitional scale), a baseline network with only image pair input is compared with a nearly identical architecture that is also given an additional rotation estimate such as from an Inertial Navigation System (INS). The inertially-aided networks show an order of magnitude improvement over the baseline when predicting rotation, but the aided rotation predictions are still worse than the input rotations. Translation predictions are not necessarily helped either. A full-trajectory analysis gives similar results. The INS-aided neural networks are also tested for sensitivity to angular random walk (ARW) and bias errors in the sensor measurements
ART-SLAM: Accurate Real-Time 6DoF LiDAR SLAM
Real-time six degrees-of-freedom pose estimation with ground vehicles represents a relevant and well-studied topic in robotics due to its many applications such as autonomous driving and 3D mapping. Although some systems already exist, they are either not accurate or they struggle in real-time settings. In this letter, we propose a fast, accurate and modular LiDAR SLAM system for both batch and online estimation. We first apply downsampling and outlier removal, to filter out noise and reduce the size of the input point clouds. Filtered clouds are then used for pose tracking, possibly aided by a pre-tracking module, and floor detection, to ground optimize the estimated trajectory. Efficient multi-steps loop closure and pose optimization, achieved through a g2o pose graph, are the last steps of the proposed SLAM pipeline. We compare the performance of our system with state-of-the-art point cloud-based methods, LOAM, LeGO-LOAM, A-LOAM, LeGO-LOAM-BOR, LIO-SAM and HDL, and show that the proposed system achieves equal or better accuracy and can easily handle even cases without loops. The comparison is done evaluating the estimated trajectory displacement using the KITTI (urban driving) and Chilean (underground mine) datasets
- …