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

Mielle, Malcolm; Magnusson, Martin; Lilienthal, Achim J.

doi:10.1109/ecmr.2019.8870345

Cited by 28 publications

(15 citation statements)

References 19 publications

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“…The cost of a RADAR sensor depends on its size and technique used such as IR, radio, and UV wave [76]. If we compare two different types of RADAR and LiDAR, such as MPR (RADAR) and Velodyne VLP-16 (LiDAR), the linear range of MRP is up to 20 m and Velodyne VLP-16 LiDAR can measure up to 40 m. Moreover, the angular resolution of LiDAR and RADAR is 0.4 °and 1.8 °, respectively [77].…”

Section: Radar Radar Technique Is An Emerging Technologymentioning

confidence: 99%

Investigation of Widely Used SLAM Sensors Using Analytical Hierarchy Process

et al. 2022

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Applications of mobile robots are continuously capturing the importance in numerous areas such as agriculture, surveillance, defense, and planetary exploration to name a few. Accurate navigation of a mobile robot is highly significant for its uninterrupted operation. Simultaneous localization and mapping (SLAM) is one of the widely used techniques in mobile robots for localization and navigation. SLAM consists of front- and back-end processes, wherein the front-end includes SLAM sensors. These sensors play a significant role in acquiring accurate environmental information for further processing and mapping. Therefore, understanding the operational limits of the available SLAM sensors and data collection techniques from a single sensor or multisensors is noteworthy. In this article, a detailed literature review of widely used SLAM sensors such as acoustic sensor, RADAR, camera, Light Detection and Ranging (LiDAR), and RGB-D is provided. The performance of SLAM sensors is compared using an analytical hierarchy process (AHP) based on various key indicators such as accuracy, range, cost, working environment, and computational cost.

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Section: Radar Radar Technique Is An Emerging Technologymentioning

confidence: 99%

Investigation of Widely Used SLAM Sensors Using Analytical Hierarchy Process

et al. 2022

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“…Another method is to simply set a fixed noise/signal threshold [19]. However, doing so comes with the risk to remove important information since the noise background level varies greatly in different settings.…”

Section: A Filtering Radar Datamentioning

confidence: 99%

CFEAR Radarodometry -- Conservative Filtering for Efficient and Accurate Radar Odometry

Adolfsson¹,

Magnusson²,

Alhashimi³

et al. 2021

Preprint

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This paper presents the accurate, highly efficient, and learning-free method CFEAR Radarodometry for largescale radar odometry estimation. By using a filtering technique that keeps the k strongest returns per azimuth and by additionally filtering the radar data in Cartesian space, we are able to compute a sparse set of oriented surface points for efficient and accurate scan matching. Registration is carried out by minimizing a point-to-line metric and robustness to outliers is achieved using a Huber loss. We were able to additionally reduce drift by jointly registering the latest scan to a history of keyframes and found that our odometry method generalizes to different sensor models and datasets without changing a single parameter. We evaluate our method in three widely different environments and demonstrate an improvement over spatially cross-validated state-of-the-art with an overall translation error of 1.76% in a public urban radar odometry benchmark, running at 55 Hz merely on a single laptop CPU thread.

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“…Besides its superior range and despite its lower spatial resolution, Millimetre-Wave (MMW) radar often overcomes the shortcomings of laser, monocular, or stereo vision because it can operate successfully in dust, fog, blizzards, and poorly lit scenes [24]. In Reference [25], it is shown in the context of a SLAM system that while producing slightly less accurate maps than LiDARs, radars are capable of capturing details, such as corners and small walls.…”

Section: Radar-based Mapping and Localisationmentioning

confidence: 99%

kRadar++: Coarse-to-Fine FMCW Scanning Radar Localisation

Martini

Gadd

Newman

2020

Sensors

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This paper presents a novel two-stage system which integrates topological localisation candidates from a radar-only place recognition system with precise pose estimation using spectral landmark-based techniques. We prove that the—recently available—seminal radar place recognition (RPR) and scan matching sub-systems are complementary in a style reminiscent of the mapping and localisation systems underpinning visual teach-and-repeat (VTR) systems which have been exhibited robustly in the last decade. Offline experiments are conducted on the most extensive radar-focused urban autonomy dataset available to the community with performance comparing favourably with and even rivalling alternative state-of-the-art radar localisation systems. Specifically, we show the long-term durability of the approach and of the sensing technology itself to autonomous navigation. We suggest a range of sensible methods of tuning the system, all of which are suitable for online operation. For both tuning regimes, we achieve, over the course of a month of localisation trials against a single static map, high recalls at high precision, and much reduced variance in erroneous metric pose estimation. As such, this work is a necessary first step towards a radar teach-and-repeat (RTR) system and the enablement of autonomy across extreme changes in appearance or inclement conditions.

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

Cited by 28 publications

References 19 publications

Investigation of Widely Used SLAM Sensors Using Analytical Hierarchy Process

Investigation of Widely Used SLAM Sensors Using Analytical Hierarchy Process

CFEAR Radarodometry -- Conservative Filtering for Efficient and Accurate Radar Odometry

kRadar++: Coarse-to-Fine FMCW Scanning Radar Localisation

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