13 accepted papers
Visual–Inertial Odometry (VIO) provides smooth, high-rate state estimates and has been widely used for robotic navigation in both terrestrial and planetary applications. However, its performance is typically dependent on the frequency of visual updates, which is a challenge for planetary rovers oper…
The iterative closest point registration algorithm has been a preferred method for light detection and ranging LiDAR-based robot localization for nearly a decade. However, even in modern simultaneous localization and mapping (SLAM) solutions, ICP can degrade and become unreliable in geometrically il…
Understanding terrain topology at long-range is crucial for the success of off-road robotic missions, especially when navigating at high-speeds. LiDAR sensors, which are currently heavily relied upon for geometric mapping, provide sparse measurements when mapping at greater distances. To address thi
Autonomous robot navigation in off–road environments requires a comprehensive understanding of the terrain geometry and traversability. The degraded perceptual conditions and sparse geometric information at longer ranges make the problem challenging especially when driving at high speeds. Furthermor
Enabling robot autonomy in complex environments for mission critical application requires robust state estimation. Particularly under conditions where the exteroceptive sensors, which the navigation depends on, can be degraded by environmental challenges thus, leading to mission failure. It is preci
In this paper, we deal with the problem of creating globally consistent pose graphs in a centralized multi-robot SLAM framework. For each robot to act autonomously, individual onboard pose estimates and maps are maintained, which are then communicated to a central server to build an optimized global…
Enabling autonomous operation of large-scale construction machines, such as excavators, can bring key benefits for human safety and operational opportunities for applications in dangerous and hazardous environments. To facilitate robot autonomy, robust and accurate state-estimation remains a core co…
LiDAR-based localization and mapping is one of the core components in many modern robotic systems due to the direct integration of range and geometry, allowing for precise motion estimation and generation of high quality maps in real-time. Yet, as a consequence of insufficient environmental constrai…
Despite the progress in legged robotic locomotion, autonomous navigation in unknown environments remains an open problem. Ideally, the navigation system utilizes the full potential of the robots' locomotion capabilities while operating within safety limits under uncertainty. The robot must sense and…
Reliable robot pose estimation is a key building block of many robot autonomy pipelines, with LiDAR localization being an active research domain. In this work, a versatile self-supervised LiDAR odometry estimation method is presented, in order to enable the efficient utilization of all available LiD…
This paper presents a novel strategy for autonomous graph-based exploration path planning in subterranean environments. Attuned to the fact that subterranean settings, such as underground mines, are often large-scale networks of narrow tunnel-like and multi-branched topologies, the proposed planner…
This paper proposes an approach for fusing direct radiometric data from a thermal camera with inertial measurements to extend the robotic capabilities of aerial robots for navigation in GPS-denied and visually degraded environments in the conditions of darkness and in the presence of airborne obscur…
This paper presents a novel path planning algorithm for autonomous, uncertainty-aware exploration and mapping of unknown environments using aerial robots. The proposed planner follows a two-step, receding horizon, belief space-based approach. At first, in an online computed tree the algorithm finds…