Francis Colas

Comparing ICP variants on real-world data sets

Many modern sensors used for mapping produce 3D point clouds, which are typically registered together using the iterative closest point (ICP) algorithm. Because ICP has many variants whose performances depend on the environment and the sensor, hundreds of variations have been published. However, no comparison frameworks are available, leading to an arduous selection of an appropriate variant for particular experimental conditions. The first contribution of this paper consists of a protocol that allows for a comparison between ICP variants, taking into account a broad range of inputs. The second contribution is an open-source ICP library, which is fast enough to be usable in multiple real-world applications, while being modular enough to ease comparison of multiple solutions. This paper presents two examples of these field applications. The last contribution is the comparison of two baseline ICP variants using data sets that cover a rich variety of environments. Besides demonstrating the need for improved ICP methods for natural, unstructured and information-deprived environments, these baseline variants also provide a solid basis to which novel solutions could be compared. The combination of our protocol, software, and baseline results demonstrate convincingly how open-source software can push forward the research in mapping and navigation.

Brain-coupled interaction for semi-autonomous navigation of an assistive robot

This paper presents a novel semi-autonomous navigation strategy designed for low throughput interfaces. A mobile robot (e.g. intelligent wheelchair) proposes the most probable action, as analyzed from the environment, to a human user who can either accept or reject the proposition. In the case of refusal, the robot will propose another action, until both entities agree on what needs to be done. In an unknown environment, the robotic system first extracts features so as to recognize places of interest where a human-robot interaction should take place (e.g. crossings). Based on the local topology, relevant actions are then proposed, the user providing answers by means of a button or a brain-computer interface (BCI). Our navigation strategy is successfully tested both in simulation and with a real robot, and a feasibility study for the use of a BCI confirms the potential of such an interface.

Tracking a depth camera: Parameter exploration for fast ICP

The increasing number of ICP variants leads to an explosion of algorithms and parameters. This renders difficult the selection of the appropriate combination for a given application. In this paper, we propose a state-of-the-art, modular, and efficient implementation of an ICP library. We took advantage of the recent availability of fast depth cameras to demonstrate one application example: a 3D pose tracker running at 30 Hz. For this application, we show the modularity of our ICP library by optimizing the use of lean and simple descriptors in order to ease the matching of 3D point clouds. This tracker is then evaluated using datasets recorded along a ground truth of millimeter accuracy. We provide both source code and datasets to the community in order to accelerate further comparisons in this field.

A Review of Point Cloud Registration Algorithms for Mobile Robotics

The topic of this review is geometric registration in robotics. Registrationalgorithms associate sets of data into a common coordinate system.They have been used extensively in object reconstruction, inspection,medical application, and localization of mobile robotics. We focus onmobile robotics applications in which point clouds are to be registered.While the underlying principle of those algorithms is simple, manyvariations have been proposed for many different applications. In thisreview, we give a historical perspective of the registration problem andshow that the plethora of solutions can be organized and differentiatedaccording to a few elements. Accordingly, we present a formalizationof geometric registration and cast algorithms proposed in the literatureinto this framework. Finally, we review a few applications of thisframework in mobile robotics that cover different kinds of platforms,environments, and tasks. These examples allow us to study the specificrequirements of each use case and the necessary configuration choicesleading to the registration implementation. Ultimately, the objective ofthis review is to provide guidelines for the choice of geometric registrationconfiguration.

Challenging data sets for point cloud registration algorithms

The number of registration solutions in the literature has bloomed recently. The iterative closest point, for example, could be considered as the backbone of many laser-based localization and mapping systems. Although they are widely used, it is a common challenge to compare registration solutions on a fair base. The main limitation is to overcome the lack of accurate ground truth in current data sets, which usually cover environments only over a small range of organization levels. In computer vision, the Stanford 3D Scanning Repository pushed forward point cloud registration algorithms and object modeling fields by providing high-quality scanned objects with precise localization. We aim to provide similar high-caliber working material to the robotic and computer vision communities but with sceneries instead of objects. We propose eight point cloud sequences acquired in locations covering the environment diversity that modern robots are susceptible to encounter, ranging from inside an apartment to a woodland area. The core of the data sets consists of 3D laser point clouds for which supporting data (Gravity, Magnetic North and GPS) are given for each pose. A special effort has been made to ensure global positioning of the scanner within mm-range precision, independent of environmental conditions. This will allow for the development of improved registration algorithms when mapping challenging environments, such as those found in real-world situations.1

Experience in System Design for Human-Robot Teaming in Urban Search and Rescue

The paper describes experience with applying a user-centric design methodology in developing systems for human-robot teaming in Urban Search & Rescue. A human-robot team consists of several robots (rovers/UGVs, microcopter/UAVs), several humans at an off-site command post (mission commander, UGV operators) and one on-site human (UAV operator). This system has been developed in close cooperation with several rescue organizations, and has been deployed in a real-life tunnel accident use case. The human-robot team jointly explores an accident site, communicating using a multi-modal team interface, and spoken dialogue. The paper describes the development of this complex socio-technical system per se, as well as recent experience in evaluating the performance of this system.

Common bayesian models for common cognitive issues

How can an incomplete and uncertain model of the environment be used to perceive, infer, decide and act efficiently? This is the challenge that both living and artificial cognitive systems have to face. Symbolic logic is, by its nature, unable to deal with this question. The subjectivist approach to probability is an extension to logic that is designed specifically to face this challenge. In this paper, we review a number of frequently encountered cognitive issues and cast them into a common Bayesian formalism. The concepts we review are ambiguities, fusion, multimodality, conflicts, modularity, hierarchies and loops. First, each of these concepts is introduced briefly using some examples from the neuroscience, psychophysics or robotics literature. Then, the concept is formalized using a template Bayesian model. The assumptions and common features of these models, as well as their major differences, are outlined and discussed.

Regional topological segmentation based on mutual information graphs

When people communicate with robots, the most intuitive mean is by naming the different regions in the environment. The capability that robots are able to identify different regions highly depends on the unsupervised topological segmentation results. This paper addresses the problem of segmenting a metric map into regions. Nowadays many researches in this direction develop approaches based on spectral clustering. However there are inherent drawbacks of spectral clustering algorithms. In this paper, we first discuss these drawbacks using several testing results; then we propose our approach based on information theory which uses Chow-Liu tree to segment the composed graph according to the weight differences. The results show that our method provides more flexible and faster results in the sense of facilitating semantic mapping or further applications.

Long-term 3D map maintenance in dynamic environments

New applications of mobile robotics in dynamic urban areas require more than the single-session geometric maps that have dominated simultaneous localization and mapping (SLAM) research to date; maps must be updated as the environment changes and include a semantic layer (such as road network information) to aid motion planning in dynamic environments. We present an algorithm for long-term localization and mapping in real time using a three-dimensional (3D) laser scanner. The system infers the static or dynamic state of each 3D point in the environment based on repeated observations. The velocity of each dynamic point is estimated without requiring object models or explicit clustering of the points. At any time, the system is able to produce a most-likely representation of underlying static scene geometry. By storing the time history of velocities, we can infer the dominant motion patterns within the map. The result is an online mapping and localization system specifically designed to enable long-term autonomy within highly dynamic environments. We validate the approach using data collected around the campus of ETH Zurich over seven months and several kilometers of navigation. To the best of our knowledge, this is the first work to unify long-term map update with tracking of dynamic objects.

A Markov semi-supervised clustering approach and its application in topological map extraction

In this paper, we present a novel semi-supervised clustering approach based on Markov process. It deals with data which include abundant local constraints. We apply the designed model to a topological region extraction problem, where topological segmentation is constructed based on sparse human inputs (potentially provided by human experts). The model considers human indications as seeds for topological regions, i.e. the partially labeled data. It results in a regional topological segmentation of connected free space.