Computing occupancy grids from multiple sensors using linear opinion pools

Adarve, Juan David; Perrollaz, Mathias; Makris, Alexandros; Laugier, Christian

doi:10.1109/icra.2012.6224976

Cited by 42 publications

(31 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The algorithm inputs are sequences of occupancy grids computed from the two lidars. To merge the eight laser scan layers acquired by the two lidars, we use a method similar to [13]. The presented algorithm has been implemented on the Nvidia Cuda parallel architecture in order to run on a Graphic Processing Unit (GPU).…”

Section: Resultsmentioning

confidence: 99%

Hybrid sampling Bayesian Occupancy Filter

Nègre

Rummelhard

Laugier

2014

2014 IEEE Intelligent Vehicles Symposium Proceedings

Self Cite

View full text Add to dashboard Cite

Abstract-Modeling and monitoring dynamic environments is a complex task but is crucial in the field of intelligent vehicle. A traditional way of addressing these issues is the modeling of moving objects, through Detection And Tracking of Moving Objects (DATMO) methods. An alternative to a classic object model framework is the occupancy grid filtering domain. Instead of segmenting the scene into objects and track them, the environment is represented as a regular grid of occupancy, in which each cell is tracked at a sub-object level. The Bayesian Occupancy Filter [1] is a generic occupancy grid framework which predicts the spread of spatial occupancy by estimating cell velocity distributions. However its velocity model, corresponding to a transition histogram per cell, leads to huge data management which in practice makes it hardly compatible to severe computational and hardware constraints, like in many embedded systems. In this paper, we present a new representation for the BOF, describing the environment through a mix of static and dynamic occupancy. This differentiation enables the use of a model adapted to the considered nature: static occupancy is described in a classic occupancy grid, while dynamic occupancy is modeled by a set of moving particles. Both static and dynamic parts are jointly generated and evaluated, their distribution over the cells being adjusted. This approach leads to a more compact model and to drastically improve the accuracy of the results, in particular in term of velocities. Experimental results show that the number of values required to model the velocities have been reduced from a typical 900 per cell (for a 30x30 neighborhood) to less than 2 per cell in average. The massive data compression allows to plan dedicated embedded devices.

show abstract

Section: Resultsmentioning

confidence: 99%

Hybrid sampling Bayesian Occupancy Filter

Nègre

Rummelhard

Laugier

2014

2014 IEEE Intelligent Vehicles Symposium Proceedings

Self Cite

View full text Add to dashboard Cite

show abstract

“…In [9], the author introduced analytical models of the distribution p(z|o i ) which allows to deduce the occupancy probability without doing any enumeration. Other authors approximated directly P [o i |z] with various analytical models [10], [4], [11]. As a result, a large majority of authors model the occupancy probability as a continuous function of the distance from the sensor, as depicted in Figure 2.…”

Section: A Theoretical Backgroundmentioning

confidence: 99%

Multi-sensor fusion of occupancy grids based on integer arithmetic

Rakotovao

Mottin

Puschini

et al. 2016

2016 IEEE International Conference on Robotics and Automation (ICRA)

Self Cite

View full text Add to dashboard Cite

For the last 25 years, occupancy grids have been intensively used as a well-understood framework for many robotic applications, such as path planning or obstacle avoidance. They offer a unifying framework for multiple heterogeneous sensor integration using a probabilistic representation of sensor data. This integration is computed through Bayesian techniques or evidence combination approaches, both requiring high computation workload using real number representation. In critical application domains, it is challenging to fuse data coming out of several sensors in real-time using constrained embedded platforms. In this paper, we propose a revised theoretical formulation of multi-sensor fusion using only integer arithmetic. We apply this novel framework to compute occupancy grid by only using integer numbers to represent probabilities. Compared to the state-of-the-art solutions, our fusion framework enables implementation on platforms with no floating-point support. Our experiments demonstrate that fusion of real automotive data from a 4-scans LIDAR can be integrated into a microcontroller without a floating-point unit. Our approach opens the perspective for microcontroller or even for hardware block based on ASIC or FPGA to support occupancy grid applications with real-time performance.

show abstract

“…Each layer is used to compute an occupancy grid using the classical approach described in [18]. In order to retrieve a single grid for representation of the environment, the data from all these layers are merged using the approach described in [19]. This approach fuses the sensory information by using Linear Opinion Pools [20].…”

Section: A Multi Layer Lidars Fusionmentioning

confidence: 99%

Using fast classification of static and dynamic environment for improving Bayesian occupancy filter (BOF) and tracking

Baig

Perrollaz

Nascimento

et al. 2012

2012 12th International Conference on Control Automation Robotics &Amp; Vision (ICARCV)

Self Cite

View full text Add to dashboard Cite

In this paper we present some important improvements to a fast motion detection technique based on laser data and odometry/imu information. This technique instead of performing a complete SLAM (Simultaneous Localization and Mapping) solution, is based on transferring occupancy information between two consecutive data grids. Then we show its integration with Bayesian Occupancy Filter (BOF) and with the subsequent tracking module called Fast Clustering-Tracking Algorithm (FCTA). We especially show the improvements achieved in tracking results after this integration.

show abstract

Computing occupancy grids from multiple sensors using linear opinion pools

Cited by 42 publications

References 13 publications

Hybrid sampling Bayesian Occupancy Filter

Hybrid sampling Bayesian Occupancy Filter

Multi-sensor fusion of occupancy grids based on integer arithmetic

Using fast classification of static and dynamic environment for improving Bayesian occupancy filter (BOF) and tracking

Contact Info

Product

Resources

About