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

Milli-RIO: Ego-Motion Estimation with Low-Cost Millimetre-Wave Radar

Robust indoor ego-motion estimation has attracted significant interest in the last decades due to the fast-growing demand for location-based services in indoor environments. Among various solutions, frequency-modulated continuous-wave (FMCW) radar sensors in millimeter-wave (MMWave) spectrum are gaining more prominence due to their intrinsic advantages such as penetration capability and high accuracy. Single-chip low-cost MMWave radar as an emerging technology provides an alternative and complementary solution for robust ego-motion estimation, making it feasible in resource-constrained platforms thanks to low-power consumption and easy system integration. In this paper, we introduce Milli-RIO, an MMWave radar-based solution making use of a single-chip low-cost radar and inertial measurement unit sensor to estimate six-degrees-of-freedom ego-motion of a moving radar. Detailed quantitative and qualitative evaluations prove that the proposed method achieves precisions on the order of few centimeters for indoor localization tasks.Comment: Submitted to IEEE Sensors, 9page

4DEgo: ego-velocity estimation from high-resolution radar data

Automotive radars allow for perception of the environment in adverse visibility and weather conditions. New high-resolution sensors have demonstrated potential for tasks beyond obstacle detection and velocity adjustment, such as mapping or target tracking. This paper proposes an end-to-end method for ego-velocity estimation based on radar scan registration. Our architecture includes a 3D convolution over all three channels of the heatmap, capturing features associated with motion, and an attention mechanism for selecting significant features for regression. To the best of our knowledge, this is the first work utilizing the full 3D radar heatmap for ego-velocity estimation. We verify the efficacy of our approach using the publicly available ColoRadar dataset and study the effect of architectural choices and distributional shifts on performance

Use of Advance Driver Assistance System Sensors for Human Detection and Work Machine Odometry

This master thesis covers two major topics, the first is the use of Advance driver assistance system (ADAS) sensors for human detection, and second is the use of ADAS sensors for the odometry estimation of the mobile work machine. Solid-state Lidar and Automotive Radar sensors are used as the ADAS sensors. Real-time Simulink models are created for both the sensors. The data is collected from the sensors by connecting the sensors with the XPC target via CAN communication. Later the data is later sent to Robot operating system (ROS) for visualization. The testing of the Solid-state Lidar and Automotive Radar sensors has been performed in different conditions and scenarios, it isn’t limited to human detection only. Detection of cars, machines, building, fence and other multiple objects have also been tested. Moreover, the two major cases for the testing of the sensors were the static case and the dynamic case. For the static case, both the sensors were mounted on a stationary rack and the moving/stationary objects were detected by the sensors. For the dynamic case, both the sensors were mounted on the GIM mobile machine, and the machine was driven around for the sensors to detect an object in the environment. The results are promising, and it is concluded that the sensors can be used for the human detection and for some other applications as well. Furthermore, this research presents an algorithm used to estimate the complete odometry/ ego-motion of the mobile work machine. For this purpose, we are using an automotive radar sensor. Using this sensor and a gyroscope, we seek a complete odometry of the GIM mobile machine, which includes 2-components of linear speed (forward and side slip) and a single component of angular speed. Kinematic equations are calculated having the constraints of vehicle motion and stationary points in the environment. Radial velocity and the azimuth angle of the objects detected are the major components of the kinematic equations provided by the automotive radar sensor. A stationary environment is a compulsory clause in accurate estimation of radar odometry. Assuming the points detected by the automotive radar sensor are stationary, it is then possible to calculate all the three unknown components of speed. However, it is impossible to calculate all the three components using a single radar sensor, because the latter system of equation becomes singular. Literature suggests use of multiple radar sensors, however, in this research, a vertical gyroscope is used to overcome this singularity. GIM mobile machine having a single automotive radar sensor and a vertical gyroscope is used for the experimentation. The results have been compared with the algorithm presented in [32] as well as the wheel odometry of the GIM mobile machine. Furthermore, the results have also been tested with complete navigation solution (GNSS included) as a reference path