Three-dimensional models provide a volumetric representation of space which is important for a variety of robotic applications including flying robots and robots that are equipped with manipulators. In this paper, we present an open-source framework to generate volumetric 3D environment models. Our mapping approach is based on octrees and uses probabilistic occupancy estimation. It explicitly represents not only occupied space, but also free and unknown areas. Furthermore, we propose an octree map compression method that keeps the 3D models compact. Our framework is available as an open-source C++ library and has already been successfully applied in several robotics projects. We present a series of experimental results carried out with real robots and on publicly available real-world datasets. The results demonstrate that our approach is able to update the representation efficiently and models the data consistently while keeping the memory requirement at a minimum.

/pdf/octomap-an-efficient-probabilistic-3d-mapping-framework-3dp42nnofi.pdf

OctoMap: an efficient probabilistic 3D mapping framework based on octrees

Three-dimensional models provide a volumetric representation of space which is important for a variety of robotic applications including flying robots and robots that are equipped with manipulators. In this paper, we present an open-source framework to generate volumetric 3D environ- ment models. Our mapping approach is based on octrees and uses probabilistic occupancy estimation. It explicitly repre- sents not only occupied space, but also free and unknown areas. Furthermore, we propose an octree map compression method that keeps the 3D models compact. Our framework is available as an open-source C++ library and has already been successfully applied in several robotics projects. We present a series of experimental results carried out with real robots and on publicly available real-world datasets. The re- sults demonstrate that our approach is able to update the representation efficiently and models the data consistently while keeping the memory requirement at a minimum.

An Efficient Probabilistic 3D Mapping Framework Based on Octrees

In this paper, we address the problem of creating an objective benchmark for evaluating SLAM approaches. We propose a framework for analyzing the results of a SLAM approach based on a metric for measuring the error of the corrected trajectory. This metric uses only relative relations between poses and does not rely on a global reference frame. This overcomes serious shortcomings of approaches using a global reference frame to compute the error. Our method furthermore allows us to compare SLAM approaches that use different estimation techniques or different sensor modalities since all computations are made based on the corrected trajectory of the robot.

We provide sets of relative relations needed to compute our metric for an extensive set of datasets frequently used in the robotics community. The relations have been obtained by manually matching laser-range observations to avoid the errors caused by matching algorithms. Our benchmark framework allows the user to easily analyze and objectively compare different SLAM approaches.

/pdf/on-measuring-the-accuracy-of-slam-algorithms-4nkbhz341e.pdf

On measuring the accuracy of SLAM algorithms

Present object detection methods working on 3D range data are so far either optimized for unstructured offroad environments or flat urban environments. We present a fast algorithm able to deal with tremendous amounts of 3D Lidar measurements. It uses a graph-based approach to segment ground and objects from 3D lidar scans using a novel unified, generic criterion based on local convexity measures. Experiments show good results in urban environments including smoothly bended road surfaces.

/pdf/segmentation-of-3d-lidar-data-in-non-flat-urban-environments-5ampgsi1u5.pdf

Segmentation of 3D lidar data in non-flat urban environments using a local convexity criterion

The article deals with the analysis and interpretation of dynamic scenes typical of urban driving. The key objective is to assess risks of collision for the ego-vehicle. We describe our concept and methods, which we have integrated and tested on our experimental platform on a Lexus car and a driving simulator. The on-board sensors deliver visual, telemetric and inertial data for environment monitoring. The sensor fusion uses our Bayesian Occupancy Filter for a spatio-temporal grid representation of the traffic scene. The underlying probabilistic approach is capable of dealing with uncertainties when modeling the environment as well as detecting and tracking dynamic objects. The collision risks are estimated as stochastic variables and are predicted for a short period ahead with the use of Hidden Markov Models and Gaussian processes. The software implementation takes advantage of our methods, which allow for parallel computation. Our tests have proven the relevance and feasibility of our approach for improving the safety of car driving.

/pdf/probabilistic-analysis-of-dynamic-scenes-and-collision-risks-wrwulfq5pt.pdf

Probabilistic Analysis of Dynamic Scenes and Collision Risks Assessment to Improve Driving Safety

Providing a robot with a fully detailed map is one appealing key for the Simultaneous Localisation and Mapping (SLAM) problem. It gives the robot a lot of hints to solve either the data association or the localisation problem itself. The more details are in the map, the more chances are that different places may appear differently, solving ambiguities. The more landmarks are used, the more accurate are the algorithms that solve the localisation problem since in a least square sense an approximation of the solution is more precise. Last, it helps a lot in the presence of a few dynamic objects because these moving parts of the environment remain marginal in the amount of data used to model the map and can thus be filtered out. For instance, the moving objects can be detected or cancelled in the localisation procedure by robust techniques using Monte-Carlo algorithms [6] or RANSAC [4].

/pdf/update-policy-of-dense-maps-efficient-algorithms-and-sparse-24wpmfg0jk.pdf

Update Policy of Dense Maps: Efficient Algorithms and Sparse Representation

The Bayesian Occupation Filter (BOF) has proven successful for target tracking in the context of automotive applications. This paper describes an improved BOF for target tracking with lower computational costs while retaining the key advantages of the original BOF formulation. The BOF takes the form of a grid based decomposition of the environment. Sensory data provides information on the probability of occupancy for each cell of the BOF grid. In contrast to the original BOF, each cell of the newly proposed BOF contains a distribution over the velocity of the propagating cell occupancy. The distribution of the velocity for each cell occupancy can be estimated using a Bayesian filtering mechanism. An inevitable problem when using a grid space representation especially in dynamic environments is discretization. A method is proposed in this paper to deal with the discretization problem. Object based representations does not exist in the BOF grids. How- ever, there are often applications which requires the definition and track- ing at the object level. A general grid based clustering and standard target tracking methodology can be applied to obtain this object level representation. To demonstrate the generality and robustness of the clustering tracking methodology when applied to the BOF framework, experiments based on tracking humans in indoor environment were conducted. The Joint Probabilistic Data Association (JPDA) algorithm has been applied to publicly available data from the European Project CAVIAR, taken from an indoor shopping center.

An Efficient Formulation of the Bayesian Occupation Filter for Target Tracking in Dynamic Environments

Building Occupancy Grids (OGs) in order to model the surrounding environment of a vehicle requires the fusion of occupancy information provided by the different embedded sensors in the same grid. The principal difficulty comes from the fact that each can have a different resolution, and also that the resolution of some sensors varies with the location in the field of view. In this paper, we present a new efficient approach to this issue, based on a Graphical Processor Unit (GPU). In that perspective, we explain why the problem of switching coordinate systems is an instance of the texture mapping problem in computer graphics. We also present an exact algorithm in order to evaluate the accuracy of such a device, which is not precisely known due to the several approximations made by the hardware. To validate our method, the results with GPU are also compared to results obtained through the exact approach and the GPU precision is shown to be good enough for robotic applications. Therefore, we describe a whole...

https://hal.inria.fr/inria-00181445/document

Efficient GPU-based construction of occupancy grids using several laser range-finders

The Bayesian Occupation Filter (BOF) has proven successful for target tracking in the context of automotive applications. This paper describes an improved BOF for target tracking with lower computational costs while retaining the key advantages of the original BOF formulation. The BOF takes the form of a grid-based decomposition of the environment. In contrast to the original BOF, each cell of the newly proposed BOF contains an additional distribution over the velocity of the propagating cell occupancy. This is estimated using Bayesian filtering. We propose how to deal with the grid discretisation problem. Object-based representations do not exist in the BOF grids. However, there are often applications which require the definition and tracking at the object level. A simple clustering and target tracking methodology is used to illustrate how to obtain this object level representation. Experiments based on tracking humans in indoor environment were conducted.

An efficient formulation of the Bayesian occupation filter for target tracking in dynamic environments

Building occupancy grids (OGs) in order to model the surrounding environment of a vehicle implies to fusion occupancy information provided by the different embedded sensors in the same grid. The principal difficulty comes from the fact that each can have a different resolution, but also that the resolution of some sensors varies with the location in the field of view. In this article we present a new exact approach to this issue and we explain why the problem of switching coordinate systems is an instance of the texture mapping problem in computer graphics. Therefore we introduce a calculus architecture to build occupancy grids with a graphical processor unit (GPU). Thus we present computational time results that can allow to compute occupancy grids for 50 sensors at frame rate even for a very fine grid. To validate our method, the results with GPU are compared to results obtained through the exact approach.

https://hal.inria.fr/inria-00182008/document

Manuel Yguel

Papers

Update Policy of Dense Maps: Efficient Algorithms and Sparse Representation

An Efficient Formulation of the Bayesian Occupation Filter for Target Tracking in Dynamic Environments

Efficient GPU-based construction of occupancy grids using several laser range-finders

An efficient formulation of the Bayesian occupation filter for target tracking in dynamic environments

Efficient GPU-based Construction of Occupancy Girds Using several Laser Range-finders