Are Doppler Velocity Measurements Useful for Spinning Radar Odometry?

Spinning, frequency-modulated continuous-wave (FMCW) radar has been gaining popularity for autonomous vehicle navigation. The spinning radar is chosen over the more classic automotive `fixed' radar as it is able to capture the full 360 degree field of view without requiring multiple sensors and extensive calibration. However, commercially available spinning radar systems have not previously had the ability to extract radial velocities due to the lack of repeated measurements in the same direction and fundamental hardware setup. A new firmware upgrade now makes it possible to alternate the modulation of the radar signal between azimuths. In this paper, we first present a way to use this alternating modulation to extract radial Doppler velocity measurements from single raw radar intensity scans. We then incorporate these measurements in two different modern odometry pipelines and evaluate them in progressively challenging autonomous driving environments. We show that using Doppler velocity measurements enables our odometry to continue functioning at state-of-the-art even in severely geometrically degenerate environments.

Need for Speed: Fast Correspondence-Free Lidar Odometry Using Doppler Velocity

In this paper, we present a fast, lightweight odometry method that uses the Doppler velocity measurements from a Frequency-Modulated Continuous-Wave (FMCW) lidar without data association. FMCW lidar is a recently emerging technology that enables per-return relative radial velocity measurements via the Doppler effect. Since the Doppler measurement model is linear with respect to the 6-degrees-of-freedom (DOF) vehicle velocity, we can formulate a linear continuous-time estimation problem for the velocity and numerically integrate for the 6-DOF pose estimate afterward. The caveat is that angular velocity is not observable with a single FMCW lidar. We address this limitation by also incorporating the angular velocity measurements from a gyroscope. This results in an extremely efficient odometry method that processes lidar frames at an average wall-clock time of 5.8ms on a single thread, well below the 10Hz operating rate of the lidar we tested. We show experimental results on real-world driving sequences and compare against state-of-the-art Iterative Closest Point (ICP)-based odometry methods, presenting a compelling trade-off between accuracy and computation. We also present an algebraic observability study, where we demonstrate in theory that the Doppler measurements from multiple FMCW lidars are capable of observing all 6 degrees of freedom (translational and angular velocity).

Towards Consistent Batch State Estimation Using a Time-Correlated Measurement Noise Model

In this paper, we present an algorithm for learning time-correlated measurement covariances for application in batch state estimation. We parameterize the inverse measurement covariance matrix to be block-banded, which conveniently factorizes and results in a computationally efficient approach for correlating measurements across the entire trajectory. We train our covariance model through supervised learning using the groundtruth trajectory. In applications where the measurements are time-correlated, we demonstrate improved performance in both the mean posterior estimate and the covariance (i.e., improved estimator consistency). We use an experimental dataset collected using a mobile robot equipped with a laser rangefinder to demonstrate the improvement in performance. We also verify estimator consistency in a controlled simulation using a statistical test over several trials.

Picking Up Speed: Continuous-Time Lidar-Only Odometry using Doppler Velocity Measurements

Frequency-Modulated Continuous-Wave (FMCW) lidar is a recently emerging technology that additionally enables per-return instantaneous relative radial velocity measurements via the Doppler effect. In this letter, we present the first continuous-time lidar-only odometry algorithm using these Doppler velocity measurements from an FMCW lidar to aid odometry in geometrically degenerate environments. We apply an existing continuous-time framework that efficiently estimates the vehicle trajectory using Gaussian process regression to compensate for motion distortion due to the scanning-while-moving nature of any mechanically actuated lidar (FMCW and non-FMCW). We evaluate our proposed algorithm on several real-world datasets, including publicly available ones and datasets we collected. Our algorithm outperforms the only existing method that also uses Doppler velocity measurements, and we study difficult conditions where including this extra information greatly improves performance. We additionally demonstrate state-of-the-art performance of lidar-only odometry with and without using Doppler velocity measurements in nominal conditions. Code for this project can be found at: https://github.com/utiasASRL/steam_icp.

Should Radar Replace Lidar in All-Weather Mapping and Localization?

Radar is a rich sensing modality that is a compelling alternative to lidar for its inherent robustness to precipitation, dust, and fog. In this paper, we investigate radar-based mapping and localization as an alternative to lidar by demonstrating the performance of our own radar-based pipeline. We present extensive comparisons against a lidar-based pipeline across varying seasonal and weather conditions using our own publicly available dataset collected on a repeated route over the course of one year. Our experiments show that while lidar achieves better overall localization accuracy, our radar-based pipeline achieves comparable SE(2) results using a fraction of the computational and memory resources required by the lidar-based pipeline. We additionally demonstrate our radar-based pipeline localizing against lidar maps with better accuracy than the current state of the art for radar-to-lidar localization. Code for this project can be found at: https://github.com/utiasASRL/vtr3

Boreas: A Multi-Season Autonomous Driving Dataset

The Boreas dataset was collected by driving a repeated route over the course of one year, resulting in stark seasonal variations and adverse weather conditions such as rain and falling snow. In total, the Boreas dataset contains over 350km of driving data featuring a 128-channel Velodyne Alpha-Prime lidar, a 360 degree Navtech CIR304-H scanning radar, a 5MP FLIR Blackfly S camera, and centimetre-accurate post-processed ground truth poses. At launch, our dataset will support live leaderboards for odometry, metric localization, and 3D object detection. The dataset and development kit are available at: https://www.boreas.utias.utoronto.ca

Radar Odometry Combining Probabilistic Estimation and Unsupervised Feature Learning

This paper presents a radar odometry method that combines probabilistic trajectory estimation and deep learned features without needing groundtruth pose information. The feature network is trained unsupervised, using only the on-board radar data. With its theoretical foundation based on a data likelihood objective, our method leverages a deep network for processing rich radar data, and a non-differentiable classic estimator for probabilistic inference. We provide extensive experimental results on both the publicly available Oxford Radar RobotCar Dataset and an additional 100 km of driving collected in an urban setting. Our sliding-window implementation of radar odometry outperforms most hand-crafted methods and approaches the current state of the art without requiring a groundtruth trajectory for training. We also demonstrate the effectiveness of radar odometry under adverse weather conditions. Code for this project can be found at: https://github.com/utiasASRL/hero_radar_odometry

Unsupervised Learning of Lidar Features for Use in a Probabilistic Trajectory Estimator

We present unsupervised parameter learning in a Gaussian variational inference setting that combines classic trajectory estimation for mobile robots with deep learning for rich sensor data, all under a single learning objective. The framework is an extension of an existing system identification method that optimizes for the observed data likelihood, which we improve with modern advances in batch trajectory estimation and deep learning. Though the framework is general to any form of parameter learning and sensor modality, we demonstrate application to feature and uncertainty learning with a deep network for 3D lidar odometry. Our framework learns from only the on-board lidar data, and does not require any form of groundtruth supervision. We demonstrate that our lidar odometry performs better than existing methods that learn the full estimator with a deep network, and comparable to state-of-the-art ICP-based methods on the KITTI odometry dataset. We additionally show results on lidar data from the Oxford RobotCar dataset.

Zeus: A System Description of the Two-Time Winner of the Collegiate SAE AutoDrive Competition

The SAE AutoDrive Challenge is a three-year collegiate competition to develop a self-driving car by 2020. The second year of the competition was held in June 2019 at MCity, a mock town built for self-driving car testing at the University of Michigan. Teams were required to autonomously navigate a series of intersections while handling pedestrians, traffic lights, and traffic signs. Zeus is aUToronto's winning entry in the AutoDrive Challenge. This article describes the system design and development of Zeus as well as many of the lessons learned along the way. This includes details on the team's organizational structure, sensor suite, software components, and performance at the Year 2 competition. With a team of mostly undergraduates and minimal resources, aUToronto has made progress towards a functioning self-driving vehicle, in just two years. This article may prove valuable to researchers looking to develop their own self-driving platform.

Variational Inference with Parameter Learning Applied to Vehicle Trajectory Estimation

We present parameter learning in a Gaussian variational inference setting using only noisy measurements (i.e., no groundtruth). This is demonstrated in the context of vehicle trajectory estimation, although the method we propose is general. The paper extends the Exactly Sparse Gaussian Variational Inference (ESGVI) framework, which has previously been used for large-scale nonlinear batch state estimation. Our contribution is to additionally learn parameters of our system models (which may be difficult to choose in practice) within the ESGVI framework. In this paper, we learn the covariances for the motion and sensor models used within vehicle trajectory estimation. Specifically, we learn the parameters of a white-noise-on-acceleration motion model and the parameters of an Inverse-Wishart prior over measurement covariances for our sensor model. We demonstrate our technique using a 36 km dataset consisting of a car using lidar to localize against a high-definition map; we learn the parameters on a training section of the data and then show that we achieve high-quality state estimates on a test section, even in the presence of outliers.