2D SLAM Correction Prediction in Large Scale Urban Environments

Alsayed, Zayed; Bresson, Guillaume; Verroust-Blondet, Anne; Nashashibi, Fawzi

doi:10.1109/icra.2018.8460773

Cited by 13 publications

(7 citation statements)

References 25 publications

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“…Almqvist et al [Almqvist et al, 2018] applied several threshold-and machine-learning-based methods to classify misaligned point clouds. Alsayed et al [Alsayed et al, 2017, Alsayed et al, 2018 presented a machine-learning-based failure detection method for 2D LiDAR SLAM. Similar approaches using GNSS is also presented in [Hsu, 2017].…”

Section: Reliabilitymentioning

confidence: 99%

Reliable Monte Carlo Localization for Mobile Robots

Akai¹

2022

Preprint

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Reliability is a key factor for realizing safety guarantee of full autonomous robot systems. In this paper, we focus on reliability in mobile robot localization. Monte Carlo localization (MCL) is widely used for mobile robot localization. However, it is still difficult to guarantee its safety because there are no methods determining reliability for MCL estimate. This paper presents a novel localization framework that enables robust localization, reliability estimation, and quick re-localization, simultaneously. The presented method can be implemented using similar estimation manner to that of MCL. The method can increase localization robustness to environment changes by estimating known and unknown obstacles while performing localization; however, localization failure of course occurs by unanticipated errors. The method also includes a reliability estimation function that enables us to know whether localization has failed. Additionally, the method can seamlessly integrate a global localization method via importance sampling. Consequently, quick re-localization from failures can be realized while mitigating noisy influence of global localization. Through three types of experiments, we show that reliable MCL that performs robust localization, self-failure detection, and quick failure recovery can be realized.

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Section: Reliabilitymentioning

confidence: 99%

Reliable Monte Carlo Localization for Mobile Robots

Akai¹

2022

Preprint

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“…The numerical value of each cell’s occupancy level is updated based on probability theory [6] or belief theory [7]. Static maps in large-scale traffic environment are constructed using LiDAR sensors with probabilistic and belief approaches [8,9,10]. Moras et al presented an occupancy grid framework that generates a global static map and classifies local moving objects simultaneously [11,12,13].…”

Section: Previous Studiesmentioning

confidence: 99%

Rapid Motion Segmentation of LiDAR Point Cloud Based on a Combination of Probabilistic and Evidential Approaches for Intelligent Vehicles

Lee

Kim

et al. 2019

Sensors

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Point clouds from light detecting and ranging (LiDAR) sensors represent increasingly important information for environmental object detection and classification of automated and intelligent vehicles. Objects in the driving environment can be classified as either dynamic or static depending on their movement characteristics. A LiDAR point cloud is also segmented into dynamic and static points based on the motion properties of the measured objects. The segmented motion information of a point cloud can be useful for various functions in automated and intelligent vehicles. This paper presents a fast motion segmentation algorithm that segments a LiDAR point cloud into dynamic and static points in real-time. The segmentation algorithm classifies the motion of the latest point cloud based on the LiDAR’s laser beam characteristics and the geometrical relationship between consecutive LiDAR point clouds. To accurately and reliably estimate the motion state of each LiDAR point considering the measurement uncertainty, both probability theory and evidence theory are employed in the segmentation algorithm. The probabilistic and evidential algorithm segments the point cloud into three classes: dynamic, static, and unknown. Points are placed in the unknown class when LiDAR point cloud is not sufficient for motion segmentation. The point motion segmentation algorithm was evaluated quantitatively and qualitatively through experimental comparisons with previous motion segmentation methods.

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“…To overcome the problems, a previous study presented a method to manage the grid map by tiles that split the space into many small areas [23]. However, since this tiling method can have a different configuration depending on the implementation of grid map, it is difficult to share or reuse the grid map by other vehicles.…”

Section: Cloud Update Of Eogmsmentioning

confidence: 99%

Cloud Update of Tiled Evidential Occupancy Grid Maps for the Multi-Vehicle Mapping

Cho

Kim

et al. 2018

Sensors

Self Cite

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Nowadays, many intelligent vehicles are equipped with various sensors to recognize their surrounding environment and to measure the motion or position of the vehicle. In addition, the number of intelligent vehicles equipped with a mobile Internet modem is increasing. Based on the sensors and Internet connection, the intelligent vehicles are able to share the sensor information with other vehicles via a cloud service. The sensor information sharing via the cloud service promises to improve the safe and efficient operation of the multiple intelligent vehicles. This paper presents a cloud update framework of occupancy grid maps for multiple intelligent vehicles in a large-scale environment. An evidential theory is applied to create the occupancy grid maps to address sensor disturbance such as measurement noise, occlusion and dynamic objects. Multiple vehicles equipped with LiDARs, motion sensors, and a low-cost GPS receiver create the evidential occupancy grid map (EOGM) for their passing trajectory based on GraphSLAM. A geodetic quad-tree tile system is applied to manage the EOGM, which provides a common tiling format to cover the large-scale environment. The created EOGM tiles are uploaded to EOGM cloud and merged with old EOGM tiles in the cloud using Dempster combination of evidential theory. Experiments were performed to evaluate the multiple EOGM mapping and the cloud update framework for large-scale road environment.

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2D SLAM Correction Prediction in Large Scale Urban Environments

Cited by 13 publications

References 25 publications

Reliable Monte Carlo Localization for Mobile Robots

Reliable Monte Carlo Localization for Mobile Robots

Rapid Motion Segmentation of LiDAR Point Cloud Based on a Combination of Probabilistic and Evidential Approaches for Intelligent Vehicles

Cloud Update of Tiled Evidential Occupancy Grid Maps for the Multi-Vehicle Mapping

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