Author name cluster

Philipp Krüsi

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

3 papers

1 author row

IROS Conference 2016 Conference Paper

Collaborative navigation for flying and walking robots

Peter Fankhauser
Michael Bloesch
Philipp Krüsi
Remo Diethelm
Martin Wermelinger
Thomas Schneider 0007
Marcin Dymczyk
Marco Hutter 0001

Flying and walking robots can use their complementary features in terms of viewpoint and payload capability to the best in a heterogeneous team. To this end, we present our online collaborative navigation framework for unknown and challenging terrain. The method leverages the flying robot's onboard monocular camera to create both a map of visual features for simultaneous localization and mapping and a dense representation of the environment as an elevation map. This shared knowledge from the flying platform enables the walking robot to localize itself against the global map, and plan a global path to the goal by interpreting the elevation map in terms of traversability. While following the planned path, the absolute pose corrections are fused with the legged state estimation and the elevation map is continuously updated with distance measurements from an onboard laser range sensor. This allows the legged robot to safely navigate towards the goal while taking into account any changes in the environment. In this setup, our approach is independent of external localization, relative observations between the robots, and does not require an initial guess about the pose of the robots. The presented methods are fully integrated and we demonstrate their capabilities in an experiment with a hexacopter and a quadrupedal robot.

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IROS Conference 2016 Conference Paper

Navigation planning for legged robots in challenging terrain

Martin Wermelinger
Peter Fankhauser
Remo Diethelm
Philipp Krüsi
Roland Siegwart
Marco Hutter 0001

This paper presents a framework for planning safe and efficient paths for a legged robot in rough and unstructured terrain. The proposed approach allows to exploit the distinctive obstacle negotiation capabilities of legged robots, while keeping the complexity low enough to enable planning over considerable distances in short time. We compute typical terrain characteristics such as slope, roughness, and steps to build a traversability map. This map is used to assess the costs of individual robot footprints as a function of the robot-specific obstacle negotiating capabilities for steps, gaps and stairs. Our sampling-based planner employs the RRT* algorithm to optimize path length and safety. The planning framework has a hierarchical architecture to frequently replan the path during execution as new terrain is perceived with onboard sensors. Furthermore, a cascaded planning structure makes use of different levels of simplification to allow for fast search in simple environments, while retaining the ability to find complex solutions, such as paths through narrow passages. The proposed navigation planning framework is integrated on the quadrupedal robot StarlETH and extensively tested in simulation as well as on the real platform.

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ICRA Conference 2014 Conference Paper

Long-term 3D map maintenance in dynamic environments

François Pomerleau
Philipp Krüsi
Francis Colas
Paul Timothy Furgale
Roland Siegwart

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.

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