A multi-sensor fusion system for moving object detection and tracking in urban driving environments

Cho, Hyunggi; Seo, Yongsok; Kumar, B. V. K. Vijaya; Rajkumar, Ragunathan

doi:10.1109/icra.2014.6907100

Cited by 352 publications

(190 citation statements)

References 14 publications

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“…NDVI used in this study was chosen for the analysis based on references belonging to other scientific disciplines such as satellite images analysis (Zha et al, 2001;Colombo et al, 2003), feature detection for self-driving cars (Cho et al, 2014) and other RPAS studies as described in the section 1. It was chosen in this study due to its capability to detect the small differences of the green and red spectra inside the images (Rasmussen et al, 2016).…”

Section: Validation Of the Use Of Ndvimentioning

confidence: 99%

Identifying Urine Patches on Intensively Managed Grassland Using Aerial Imagery Captured From Remotely Piloted Aircraft Systems

Maire

Gibson-poole

Cowan

et al. 2018

Front. Sustain. Food Syst.

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The deposition of livestock urine and feces in grazed fields results in a sizable input of available nitrogen (N) in these soils; therefore significantly increasing potential nitrogen pollution from agricultural areas in the form of nitrous oxide (N 2 O), ammonia (NH 3 ), and nitrate (NO 3 − ). Livestock deposition events contributes to high spatial variability within the field and generate uncertainties when assessing the contribution that animal waste has on nitrogen pollution pathways. This study investigated an innovative technique for identifying the spatial coverage of urine deposition in grasslands without the need for manual soil measurements. A Remotely Piloted Aircraft System (RPAS) using a twin camera system was used to identify urine patches in a 5 ha field, which had been grazed by sheep 3 weeks previous to measurements. The imagery was processed using Agisoft Photoscan (Agisoft LLC) to produce true and false color orthomosaic imagery of the entire field. Imagery of five areas (225 m 2 ) within the field were analyzed using a custom R script. For a total of 1,125 m 2 of grassland, 12.2% of the area consisted of what was classified as urine patch. A simple up-scaling method was applied to these data to calculate N 2 O emissions for the entire field providing an estimate of 1.3-2.0 kg N 2 O-N ha −1 emissions from urine and fertilizer inputs.

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Section: Validation Of the Use Of Ndvimentioning

confidence: 99%

Identifying Urine Patches on Intensively Managed Grassland Using Aerial Imagery Captured From Remotely Piloted Aircraft Systems

Maire

Gibson-poole

Cowan

et al. 2018

Front. Sustain. Food Syst.

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“…移動ロボットやビークル自らが周囲環境をセンシングして移動物体を追跡（位置や速度，大きさを推定）するシステムの構築は，様々な分野で必要不可欠な技術である．そのため，車載レーザスキャナやカメラ，ミリ波レーダを用いた移動物体追跡システムが提案され，実用化されている (Cho et al,2014, Fayad and Cherfaoui, 2007, Mertz et al, 2013, Mukhtar et al, 2015, Wang et al, 2015．複数の移動ロボットが近接する場合，通信系を介して各ロボットのセンサ情報を共有することで，移動物体が 1台のロボットの視野外や死角に存在しても他のロボットから観測できるため，移動物体追跡の精度や信頼性が向上する (Ahmad and Lima, 2013, Robin and Lacroix, 2016, Tsokas and Kyriakopoulos, 2012, Wang and Gu, 2012 , Hashimoto et al, 2014, Tamura et al, 2016．近年，マルチレイヤレーザスキャナの普及により，それを利用した環境認識の研究が盛んである (Miyata et al, 2009, Zeng, 2013 …”

Section: 緒言unclassified

Cooperative tracking of moving objects with multiple mobile robots using in-vehicle laser scanner in GNSS-denied environments

Kanaki

Hashimoto

Yoden

et al. 2017

Transactions of the JSME (In Japanese)

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This paper presents a laser-based tracking (estimation of pose and size) of moving objects using multiple mobile robots in Global-navigation-satellite-system (GNSS)-denied environments. Each robot is equipped with a multilayer laser scanner and detects moving objects, such as people, cars, and bicycles, in its own laser-scanned images by applying an occupancy-gridbased method. It then sends measurement information related to the moving objects to a central server. The central server estimates the objects' poses (positions and velocities) and sizes from the measurement information using Bayesian filter. In this cooperative-tracking method, the nearby robots always share their tracking information, allowing tracking of invisible or partially visible objects. To perform a reliable cooperative tracking, robots have to correctly identify their relative pose. To do so in GNSS-denied environments, the relative pose is estimated by scan matching using laser measurements captured by both sensor nodes. Such cooperative scan matching is performed by 4-points-congruent-sets (4PCS)-matching-based coarse registration and Iterative-closest-point (ICP)-based fine registration methods. The experimental results of tracking a car, a motorcycle, and a pedestrian with two robots in an outdoor GNSS-denied environment validate the proposed method.

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“…IMU data has been applied to improve pixel matching among images [16]. Radar, camera, and laser scanner data have been integrated in the Kalman filter to track moving objects in a scene [17]. A prototype of an autonomous vehicle, known as Google car, has been claimed to be fully operational [18].…”

Section: Literature Reviewmentioning

confidence: 99%

Tracking 3D Moving Objects Based on GPS/IMU Navigation Solution, Laser Scanner Point Cloud and GIS Data

Hosseinyalamdary

Balazadegan

Toth

2015

IJGI

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Abstract:Monitoring vehicular road traffic is a key component of any autonomous driving platform. Detecting moving objects, and tracking them, is crucial to navigating around objects and predicting their locations and trajectories. Laser sensors provide an excellent observation of the area around vehicles, but the point cloud of objects may be noisy, occluded, and prone to different errors. Consequently, object tracking is an open problem, especially for low-quality point clouds. This paper describes a pipeline to integrate various sensor data and prior information, such as a Geospatial Information System (GIS) map, to segment and track moving objects in a scene. We show that even a low-quality GIS map, such as OpenStreetMap (OSM), can improve the tracking accuracy, as well as decrease processing time. A bank of Kalman filters is used to track moving objects in a scene. In addition, we apply non-holonomic constraint to provide a better orientation estimation of moving objects. The results show that moving objects can be correctly detected, and accurately tracked, over time, based on modest quality Light Detection And Ranging (LiDAR) data, a coarse GIS map, and a fairly accurate Global Positioning System (GPS) and Inertial Measurement Unit (IMU) navigation solution.

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A multi-sensor fusion system for moving object detection and tracking in urban driving environments

Cited by 352 publications

References 14 publications

Identifying Urine Patches on Intensively Managed Grassland Using Aerial Imagery Captured From Remotely Piloted Aircraft Systems

Identifying Urine Patches on Intensively Managed Grassland Using Aerial Imagery Captured From Remotely Piloted Aircraft Systems

Cooperative tracking of moving objects with multiple mobile robots using in-vehicle laser scanner in GNSS-denied environments

Tracking 3D Moving Objects Based on GPS/IMU Navigation Solution, Laser Scanner Point Cloud and GIS Data

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