Mimicking human navigation is a challenging goal for autonomous robots. This requires to explicitly take into account not only geometric representation but also high-level interpretation of the environment. In this paper, we demonstrate the capability to infer a route in a global map by using semantics. Our approach relies on an object-based representation of the world automatically built by robots from spherical images. In addition, we propose a new approach to specify paths in terms of high-level robot actions. This path description provides robots with the ability to interact with humans in an intuitive way. We perform experiments on simulated and real-world data, demonstrating the ability of our approach to deal with complex large-scale outdoor environments whilst dealing with labelling errors.

Mapping evolving environments requires an update mechanism to efficiently deal with dynamic objects. In this context, we propose a new approach to update maps pertaining to large-scale dynamic environments with semantics. While previous works mainly rely on large amount of observations, the proposed framework is able to build a stable representation with only two observations of the environment. To do this, scene understanding is used to detect dynamic objects and to recover the labels of the occluded parts of the scene through an inference process which takes into account both spatial context and a class occlusion model. Our method was evaluated on a database acquired at two different times with an interval of three years in a large dynamic outdoor environment. The results point out the ability to retrieve the hidden classes with a precision score of 0.98. The performances in term of localisation are also improved.

Navigation in large scale environments is challenging because it requires accurate local map and global relocation ability. We present a new hybrid metric-topological-semantic map structure, called MTS-map, that allows a fine metric-based navigation and fast coarse query-based localisation. It consists of local sub-maps connected through two topological layers at metric and semantic levels. Semantic information is used to build concise local graph-based descriptions of sub-maps. We propose a robust and efficient algorithm that relies on MTS-map structure and semantic description of sub-maps to relocate very fast. We combine the discriminative power of semantics with the robustness of an interpretation tree to compare the graphs very fast and outperform state-of-the-art-techniques. The proposed approach is tested on a challenging dataset composed of more than 13000 real world images where we demonstrate the ability to relocate within 0.12ms.

We present a generative approach to perform robot mapping that is based on an intelligent integration of static and dynamic entity classes within an environment, in order to extrapolate map information at various resolutions. Our framework differentiates from the conventional standpoint where different mapping levels are overlaid on one another, by fusing information from different mapping levels that allows us to infer new information within partially mapped environments. Towards this goal, we develop a class-dependent map extrapolation function that captures the discriminative relation between an environment entity and the mapping procedure. We illustrate the advantages in using heterogeneous contextual information when mapping an environment using a prototype implementation of our approach on an indoor robot platform, giving very promising results.

We report our first experiences with Leaving Flatland, an exploratory project that studies the key challenges of closing the loop between autonomous perception and action on challenging terrain. We propose a comprehensive system for localization, mapping, and planning for the RHex mobile robot in fully 3D indoor and outdoor environments. This system integrates Visual Odometry-based localization with new techniques in real-time 3D mapping from stereo data. The motion planner uses a new decomposition approach to adapt existing 2D planning techniques to operate in 3D terrain. We test the map-building and motion-planning subsystems on real and synthetic data, and show that they have favorable computational performance for use in high-speed autonomous navigation.

The authors previously (1998) argued that the LAAS architecture is one of the most suitable for mobile robot control. This statement may seem over-optimistic, not to say pretentious and unverifiable. After all, can we compare architectures? can we set up benchmarks? or can we measure how good an architecture is compared to another? An architecture defines organization principles, integration methods and supporting tools. Comparing those tools, methods and principles may sometime end up in sterile controversies. However, we think there are means to measure the overall quality (or interest) of an architecture. Development time is for example one relevant criterion. Basically, using a specific architecture, how long does it take to integrate a complete demonstration, including nontrivial decisional capabilities, from the low level functional modules up to the supervisory level? This may seem a rather weak measure of architecture quality; however, it encompasses properties such as genericity and adaptability, ease of design and programming, extensibility and robustness. In this paper we describe our recent experience in integrating a complete demonstration from scratch in 40 days using the LAAS architecture.

Presents our project to develop a personal robot called Diligent. We first discuss the main issues involved in personal robotics. Then we analyze some of the key features implied by what we call "human-friendly navigation". Finally, we present the current state of Diligent. Diligent is already able to navigate safely and repeatedly in a relatively large environment. It is also capable of some preliminary human-robot interactions through very simple and intuitive commands.