Radar SLAM using visual features

Callmer, Jonas; Törnqvist, David; Gustafsson, Fredrik; Svensson, Henrik; Carlbom, Pelle

doi:10.1186/1687-6180-2011-71

Cited by 39 publications

(19 citation statements)

References 31 publications

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“…The first step is to extract important features from the radar scans. In [40], the amplitude gridmap accumulated from the radar scan is transformed into a grayscale image and then interesting points are detected using feature extraction techniques, e.g, SIFT. In [38], the authors use a range-compressed image to estimate the motion of an unmanned aerial aircraft.…”

Section: Radar Odometrymentioning

confidence: 99%

A Survey on Odometry for Autonomous Navigation Systems

et al. 2019

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The development of a navigation system is one of the major challenges in building a fully autonomous platform. Full autonomy requires a dependable navigation capability not only in a perfect situation with clear GPS signals but also in situations, where the GPS is unreliable. Therefore, selfcontained odometry systems have attracted much attention recently. This paper provides a general and comprehensive overview of the state of the art in the field of self-contained, i.e., GPS denied odometry systems, and identifies the out-coming challenges that demand further research in future. Self-contained odometry methods are categorized into five main types, i.e., wheel, inertial, laser, radar, and visual, where such categorization is based on the type of the sensor data being used for the odometry. Most of the research in the field is focused on analyzing the sensor data exhaustively or partially to extract the vehicle pose. Different combinations and fusions of sensor data in a tightly/loosely coupled manner and with filtering or optimizing fusion method have been investigated. We analyze the advantages and weaknesses of each approach in terms of different evaluation metrics, such as performance, response time, energy efficiency, and accuracy, which can be a useful guideline for researchers and engineers in the field. In the end, some future research challenges in the field are discussed.

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Section: Radar Odometrymentioning

confidence: 99%

A Survey on Odometry for Autonomous Navigation Systems

et al. 2019

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“…Two further examples of objective functions characterize map quality [20] and radar scan distortion [21] in terms of motion. Feature-based approaches associate keypoints using descriptors, like BASD [22] and SURF [16].…”

Section: Related Workmentioning

confidence: 99%

Radar-only ego-motion estimation in difficult settings via graph matching

Cen

Newman

2019

2019 International Conference on Robotics and Automation (ICRA)

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Radar detects stable, long-range objects under variable weather and lighting conditions, making it a reliable and versatile sensor well suited for ego-motion estimation. In this work, we propose a radar-only odometry pipeline that is highly robust to radar artifacts (e.g., speckle noise and false positives) and requires only one input parameter. We demonstrate its ability to adapt across diverse settings, from urban UK to off-road Iceland, achieving a scan matching accuracy of approximately 5.20 cm and 0.0929 deg when using GPS as ground truth (compared to visual odometry's 5.77 cm and 0.1032 deg). We present algorithms for keypoint extraction and data association, framing the latter as a graph matching optimization problem, and provide an in-depth system analysis.

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“…Schikora and Romba [12] and Callmer et al [13] use SIFT features with radar scans. Schikora and Romba [12] present an algorithm for fusion of images from multiple cameras and multiple radars, while Callmer et al [13] use the long range of the radar to estimate the movement of a vessel and perform loop closing after long travel in open water. Since our radar behaves like a low resolution lidar, extracting features on the scan is not trivial and we instead use SLAM methods based on the registration of successive scans.…”

Section: Related Workmentioning

confidence: 99%

A comparative analysis of radar and lidar sensing for localization and mapping

Mielle

Magnusson

Lilienthal

2019

2019 European Conference on Mobile Robots (ECMR)

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Lidars and cameras are the sensors most commonly used for Simultaneous Localization And Mapping (SLAM). However, they are not effective in certain scenarios, e.g. when fire and smoke are present in the environment. While radars are much less affected by such conditions, radar and lidar have rarely been compared in terms of the achievable SLAM accuracy. We present a principled comparison of the accuracy of a novel radar sensor against that of a Velodyne lidar, for localization and mapping. We evaluate the performance of both sensors by calculating the displacement in position and orientation relative to a ground-truth reference positioning system, over three experiments in an indoor lab environment. We use two different SLAM algorithms and found that the mean displacement in position when using the radar sensor was less than 0.037 m, compared to 0.011 m for the lidar. We show that while producing slightly less accurate maps than a lidar, the radar can accurately perform SLAM and build a map of the environment, even including details such as corners and small walls.

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Radar SLAM using visual features

Cited by 39 publications

References 31 publications

A Survey on Odometry for Autonomous Navigation Systems

A Survey on Odometry for Autonomous Navigation Systems

Radar-only ego-motion estimation in difficult settings via graph matching

A comparative analysis of radar and lidar sensing for localization and mapping

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