Hybrid Sampling Bayesian Occupancy Filter

International audienceModeling 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 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

Hybrid Sampling Bayesian Occupancy Filter

International audienceModeling 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 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

Robust Vision-based Underwater Target Identification & Homing Using Self-Similar Landmarks

International audienceNext generation Autonomous Underwater Vehicles (AUVs) will be required to robustly identify underwater targets for tasks such as inspection, localisation and docking. Given their often unstructured operating environments, vision offers enormous potential in underwater navigation over more traditional methods, however, reliable target segmentation often plagues these systems. This paper addresses robust vision-based target recognition by presenting a novel scale and rotationally invariant target design and recognition routine based on Self-Similar Landmarks (SSL) that enables robust target pose estimation with respect to a single camera. These algorithms are applied to an AUV with controllers developed for vision-based docking with the target. Experimental results show that system performs exceptionally on limited processing power and demonstrates how the combined vision and controller systems enables robust target identification and docking in a variety of operating conditions

A Comparison of Three Methods for Measure of Time to Contact

International audienceTime to Contact (TTC) is a biologically inspired method for obstacle detection and reactive control of motion that does not require scene reconstruction or 3D depth estimation. Estimating TTC is difficult because it requires a stable and reliable estimate of the rate of change of distance between image features. In this paper we propose a new method to measure time to contact, Active Contour Affine Scale (ACAS). We experimentally and analytically compare ACAS with two other recently proposed methods: Scale Invariant Ridge Segments (SIRS), and Image Brightness Derivatives (IBD). Our results show that ACAS provides a more accurate estimation of TTC when the image flow may be approximated by an affine transformation, while SIRS provides an estimate that is generally valid, but may not always be as accurate as ACAS, and IBD systematically over-estimate time to contact

Scale Invariant Detection and Tracking of Elongated Structures

International audienceThis paper describes a method for the detection of tracking of elongated structures that is robust under changes of scale and orientation. This method is based on extending the concept of scale invariant natural interest points to include elongated ridge structures. An operator is proposed that directly detects ridge points and provides an estimation of their elongation and orientation. A tracking process is used to follow elongated features over time and to robustly observe changes in scale and orientation. Changes in scale are used to directly estimate time to contact. Experimental results demonstrate that the method works well in cluttered scenes that are typical of urban environments

Probabilistic Grid-based Collision Risk Prediction for Driving Application

International audienceIn the recent years, more and more modern cars have been equipped with perception capabilities. One of the key applications of such perception systems is the estimation of a risk of collision. This is necessary for both Advanced Driver Assistance Systems and Autonomous Navigation. Most approach for risk estimation propose to detect and track the dynamic objects in the scene. Then the risk is estimated as a Time To Collision (TTC) by projecting the object's trajectory in the future. In this paper, we propose a new grid-based approach for collision risk prediction, based on the Hybrid-Sampling Bayesian Occupancy Filter framework. The idea is to compute an estimation of the TTC for each cell of the grid, instead of reasoning on objects. This strategy avoids to solve the difficult problem of multi-objects detection and tracking and provides a probabilistic estimation of the risk associated to each TTC value. After promising initial results, we propose in this paper to evaluate the relevance of the method for real on-road applications, by using a real-time implementation of our method in an experimental vehicle

Ground Estimation and Point Cloud Segmentation using SpatioTemporal Conditional Random Field

International audienceWhether it be to feed data for an object detection-and-tracking system or to generate proper occupancy grids, 3D point cloud extraction of the ground and data classification are critical processing tasks, on their efficiency can drastically depend the whole perception chain. Flat-ground assumption or form recognition in point clouds can either lead to systematic error, or massive calculations. This paper describes an adaptive method for ground labeling in 3D Point clouds, based on a local ground elevation estimation. The system proposes to model the ground as a Spatio-Temporal Conditional Random Field (STCRF). Spatial and temporal dependencies within the segmentation process are unified by a dynamic probabilistic framework based on the conditional random field (CRF). Ground elevation parameters are estimated in parallel in each node, using an interconnected Expectation Maximization (EM) algorithm variant. The approach, designed to target high-speed vehicle constraints and performs efficiently with highly-dense (Velodyne-64) and sparser (Ibeo-Lux) 3D point clouds, has been implemented and deployed on experimental vehicle and platforms, and are currently tested on embedded systems (Nvidia Jetson TX1, TK1). The experiments on real road data, in various situations (city, countryside, mountain roads,...), show promising results

Evolution of the robotic control frameworks at INRIA Rhône-Alpes

Demos - http://car2011.inrialpes.fr/presentations-and-papers/National audienceIntense efforts have been carried out in the last decades to de ne and implement frameworks to ease the development of robotic applications. This led each research group to propose their own solution, well suited for their needs, however no common framework has been adopted. But today we have the feeling that a peculiar framework has some of the qualities required to meet with general acceptance as far robotics research is concerned : the open source robotics platform ROS developed by Willow Garage. At INRIA Rhône-Alpes, we are such a research group that developed its own framework, Hugr. In this paper, we present the requirements that ruled its design and how we now envision migrating to ROS

Integration of ADAS algorithm in a Vehicle Prototype

International audienceFor several years, INRIA and Toyota Europe have been working together in the development of algorithms directed to ADAS. This paper will describe the main results of this successful joint project, applied to a prototype vehicle equipped with several sensors. This work will detail the framework, steps taken and motivation behind the developed technologies, as well as address the requirements needed for the automobile industry

The ArosDyn Project: Robust Analysis of Dynamic Scenes

International audienceThe ArosDyn project aims to develop embedded software for robust analysis of dynamic scenes in urban traffic environments, in order to estimate and predict collision risks during car driving. The on-board telemetric sensors (lidars) and visual sensors (stereo camera) are used to monitor the environment around the car. The algorithms make use of Bayesian fusion of heterogenous sensor data. The key objective is to process sensor data for robust detection and tracking of multiple moving objects for estimating and predicting collision risks in real time, in order to help avoid potentially dangerous situations