François Pomerleau

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.

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.

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.

Autonomous Inland Water Monitoring: Design and Application of a Surface Vessel

This article presents a novel autonomous surface vessel (ASV) that was designed and manufactured specifically for the monitoring of water resources, resources that are not only constantly drained but also face the growing threat of mass proliferation (bloom) of noxious cyanobacteria. On one hand, the distribution of these blooms in a given water body requires a surveillance of biological data at high spatial resolution on both vertical and horizontal axes, whereas on the other hand, the understanding of the temporal evolution of the cyanobacteria necessitates repeated sampling at the same location. Therefore, our ASV was designed to combine the ability to take measurements within a range of depths, with its custom-made winch, and accurate localization provided by the global positioning system (GPS), without the need for static installations. This article first describes the ASV conception, and then the results of extended field tests on the waypoint navigation mode are discussed. Finally, the first results of a sampling campaign for monitoring algal blooms in Lake Zurich are presented. This work constitutes advances in the deployment of mobile measurement platforms for environmental monitoring in lacustrine environments. Furthermore, it investigates the application of a single ASV to capture both spatial and temporal dynamics of harmful cyanobacterial blooms in lakes. Combining surface mobility with depth measurements within a single robot allows fast deployments in remote location, which is cost efficient for lake sampling. This reduces the need for fixed installations, which can be impossible in recreational areas. The high-resolution sampling of lakes will contribute to understand and predict the occurrence of harmful cyanobacterial blooms for a better management of water resources.

Egocentric and exocentric teleoperation interface using real-time, 3D video projection

The user interface is the central element of a telepresence robotic system and its visualization modalities greatly affect the operators situation awareness, and thus its performance. Depending on the task at hand and the operators preferences, going from ego- and exocentric viewpoints and improving the depth representation can provide better perspectives of the operation environment. Our system, which combines a 3D reconstruction of the environment using laser range finder readings with two video projection methods, allows the operator to easily switch from ego- to exocentric viewpoints. This paper presents the interface developed and demonstrates its capabilities by having 13 operators teleoperate a mobile robot in a navigation task.

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.

Scale-only visual homing from an omnidirectional camera

Visual Homing is the process by which a mobile robot moves to a Home position using only information extracted from visual data. The approach we present in this paper uses image keypoints (e.g. SIFT) extracted from omnidirectional images and matches the current set of keypoints with the set recorded at the Home location. In this paper, we first formulate three different visual homing problems using uncalibrated omnidirectional camera within the Image Based Visual Servoing (IBVS) framework; then we propose a novel simplified homing approach, which is inspired by IBVS, based only on the scale information of the SIFT features, with its computational cost linear to the number of features. This paper reports on the application of our method on a commonly cited indoor database where it outperforms other approaches. We also briefly present results on a real robot and allude on the integration into a topological navigation framework.

The role of homing in visual topological navigation

Visual homing has been widely studied in the past decade. It enables a mobile robot to move to a Home position using only information extracted from visual data. However, integration of homing algorithms into real applications is not widely studied and poses a number of significant challenges. Failures often occur due to moving people within the scene and variations in illumination. We present a novel integrated indoor topological navigation framework, which combines odometry motion with visual homing algorithms. We show robustness to scene variation and real-time performance through a series of tests conducted in four real apartments and several typical indoor scenes, including doorways, offices etc.