Radar-Inertial State Estimation and Obstacle Detection for Micro-Aerial Vehicles in Dense Fog

Kramer, Andrew; Heckman, Christoffer

doi:10.1007/978-3-030-71151-1_1

Cited by 11 publications

(4 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Additionally, automotive-grade system-on-chip radars are lightweight and have low power requirements, making them attractive for vehicles with limited payload and power capacity, for example, micro aerial vehicles (MAVs). Because of these attributes, the number of robotic perception papers using automotive millimeter wave radar sensors has increased drastically in the last few years (Kramer and Heckman 2021;Kramer et al, 2020;Lu et al, 2020aLu et al, , 2020bRapp et al, 2017;Schuster et al, 2016aSchuster et al, , 2016b.…”

Section: Motivation and Contributionmentioning

confidence: 99%

“…Millimeter wave radars, of the type used in advanced driver assistance systems (ADAS), are an attractive option for perception and navigation in VDEs (Kramer and Heckman 2021; Kramer et al, 2020). These sensors do not require ambient light to operate and are unaffected by airborne particulates like smoke and fog (Lu et al, 2020a).…”

Section: Introductionmentioning

confidence: 99%

“…This dataset includes unprocessed analog-to-digital converter (ADC) samples from each radar measurement, dense 3D heatmaps generated from signal processing on the raw ADC these samples containing range, doppler velocity, azimuth, and elevation data, and point targets derived from the sensor’s onboard signal processing and target detection process. We include point targets, despite their drawbacks, as they provide a familiar entry point for researches given that several recent methods have relied on them (Kramer and Heckman 2021; Kramer et al, 2020; Lu et al, 2020a,b).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

ColoRadar: The direct 3D millimeter wave radar dataset

Kramer

Kyle

Williams

et al. 2022

The International Journal of Robotics Research

Self Cite

View full text Add to dashboard Cite

This work presents two different forms of dense, high-resolution radar data from two frequency modulated continuous wave radar sensors, along sparse radar pointclouds produced by one of the radar sensors. In addition, all datasets include 3D lidar and inertial measurements, and a lidar-based simultaneous localization and mapping pose estimation. Over 2 h of 6D pose data was generated across 52 datasets collected in highly diverse 3D environments including lab spaces, outside and inside large buildings, urban walkways, and a mine. One dataset, from the ASPEN Lab, also includes precision groundtruth generated from a motion capture system. Intrinsic radar calibration and measured extrinsic sensor position calibrations are also provided along with python based development tools to interact with the various datasets. This data is designed to assist with generating radar based localization algorithms and calibrations between radar and other sensors.

show abstract

Section: Motivation and Contributionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

ColoRadar: The direct 3D millimeter wave radar dataset

Kramer

Kyle

Williams

et al. 2022

The International Journal of Robotics Research

Self Cite

View full text Add to dashboard Cite

show abstract

“…Radar sensors utilize signals of a lower frequency along the electromagnetic spectrum compared to camera and Li-DAR sensors. Due to the fact that signal attenuation due to atmospheric gases and rain decreases with decreasing frequency, radar sensors work better in adverse weather conditions [8,12]. This better performance coupled with their low cost are the main reasons why radar sensors are preferred in advanced driver assistance systems (ADAS) on passenger cars.…”

Section: Introductionmentioning

confidence: 99%

Continuous-time Radar-inertial Odometry for Automotive Radars

Ng¹,

Choi²,

Tan³

et al. 2022

Preprint

View full text Add to dashboard Cite

We present an approach for radar-inertial odometry which uses a continuous-time framework to fuse measurements from multiple automotive radars and an inertial measurement unit (IMU). Adverse weather conditions do not have a significant impact on the operating performance of radar sensors unlike that of camera and LiDAR sensors. Radar's robustness in such conditions and the increasing prevalence of radars on passenger vehicles motivate us to look at the use of radar for ego-motion estimation. A continuous-time trajectory representation is applied not only as a framework to enable heterogeneous and asynchronous multi-sensor fusion, but also, to facilitate efficient optimization by being able to compute poses and their derivatives in closed-form and at any given time along the trajectory. We compare our continuous-time estimates to those from a discrete-time radar-inertial odometry approach and show that our continuous-time method outperforms the discrete-time method. To the best of our knowledge, this is the first time a continuous-time framework has been applied to radar-inertial odometry.

show abstract