A probabilistic representation of LiDAR range data for efficient 3D object detection

Yapo, Theodore C.; Stewart, Charles; Radke, Richard J.

doi:10.1109/cvprw.2008.4563033

Cited by 16 publications

(7 citation statements)

References 20 publications

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“…This equation is evaluated for every voxel using the returns which pass through the volume, resulting in a volumetric map of the opacity of the entire scene. Similar methods have been shown in the work of Yapo et al 12 and Haas, 7 though our work differs in that we are interested obtaining continuous opacity values rather than empty/full status.…”

Section: Voxelization Using Our Transmission Approachmentioning

confidence: 61%

Fusing LIDAR-based voxel geometry with multi-angle visible imagery

Hagstrom

Messinger

2013

SPIE Proceedings

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Modern airborne LIDAR instruments are capable of accurately measuring fine detail, making them ideal for producing 3D models. A closely related problem is how best to map passive aerial imagery to the LIDAR-derived models. Typical solutions to this problem involve first constructing a surface representation from the LIDAR, then projecting imagery onto this surface. Unfortunately, a surface model can introduce errors into the process because it is not a good representation of the underlying scene geometry in areas containing overlapping or complex surfaces. A voxel-based 3D model of the LIDAR geometry is one alternative to the surface representation, and we show how this achieves more accurate results in complex areas when compared to existing approaches. Additional information we derive for the voxel model can also be used to assist with fusing the aerial imagery by driving quality metrics, and we demonstrate how this gives improved results. Multiple images covering the same area are required in order to capture details occluded in any single aerial photograph. We show how this occlusion affects fusion with the 3D model, and how any redundant color information can be filtered further to produce better products. Results are presented from our voxel-based fusion technique using LIDAR and coincident visible aerial imagery collected in the summer of 2011 over downtown Rochester, NY.

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Section: Voxelization Using Our Transmission Approachmentioning

confidence: 61%

Fusing LIDAR-based voxel geometry with multi-angle visible imagery

Hagstrom

Messinger

2013

SPIE Proceedings

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“…In [8], occupancy grid methods were used to detect pedestrians using lidar. In [18], a three-state model for cells included representation as occupied, hidden, or free. In [19], a weighted sum of lidar and stereo-vision observations was used, the weight based on the confidence in the sensor's measurement.…”

Section: A Compact Representationsmentioning

confidence: 99%

Dynamic Probabilistic Drivability Maps for Lane Change and Merge Driver Assistance

Sivaraman

Trivedi

2014

IEEE Trans. Intell. Transport. Syst.

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In this paper, we present a novel probabilistic compact representation of the on-road environment, i.e., the dynamic probabilistic drivability map (DPDM), and demonstrate its utility for predictive lane change and merge (LCM) driver assistance during highway and urban driving. The DPDM is a flexible representation and readily accepts data from a variety of sensor modalities to represent the on-road environment as a spatially coded data structure, encapsulating spatial, dynamic, and legal information. Using the DPDM, we develop a general predictive system for LCMs. We formulate the LCM assistance system to solve for the minimum-cost solution to merge or change lanes, which is solved efficiently using dynamic programming over the DPDM. Based on the DPDM, the LCM system recommends the required acceleration and timing to safely merge or change lanes with minimum cost. System performance has been extensively validated using real-world on-road data, including urban driving, on-ramp merges, and both dense and free-flow highway conditions.

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“…The results are accurate 3D scans of the vehicle surroundings up to 100 m distance. For a general discussion of lidar data quality and possible measurement error types, see [3] and [20].…”

Section: A Measurement Inputmentioning

confidence: 99%

Bayesian Occupancy grid Filter for dynamic environments using prior map knowledge

Gindele

Brechtel

Schröder

et al. 2009

2009 IEEE Intelligent Vehicles Symposium

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Building a model of the environment is essential for mobile robotics. It allows the robot to reason about its sourroundings and plan actions according to its intentions. To enable safe motion planning it is vital to anticipate object movements. This paper presents an improved formulation for occupancy filtering. Our approach is closely related to the Bayesian Occupancy Filter (BOF) presented in [4]. The basic idea of occupancy filters is to represent the environment as a 2-dimensional grid of cells holding information about their state of occupancy and velocity. To improve the accuracy of predictions, prior knowledge about the motion preferences is used, derived from map data that can be obtained from navigation systems. In combination with a physically accurate transition model, it is possible to estimate the environment dynamics. Experiments show that this yields reliable estimates even for occluded regions.

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A probabilistic representation of LiDAR range data for efficient 3D object detection

Cited by 16 publications

References 20 publications

Fusing LIDAR-based voxel geometry with multi-angle visible imagery

Fusing LIDAR-based voxel geometry with multi-angle visible imagery

Dynamic Probabilistic Drivability Maps for Lane Change and Merge Driver Assistance

Bayesian Occupancy grid Filter for dynamic environments using prior map knowledge

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