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Active Asteroid-SLAM

Active Graph SLAM with Landing Site Discovery in a Deep Space Proximity Operations Scenario

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Abstract

In this paper, we propose an active real-time capable 3D graph based simultaneous localization and mapping (Graph SLAM) approach, which actively estimates the state of an autonomous spacecraft relative to a simultaneously established map estimate. The graph is constructed in a tightly-coupled fashion, where an Extended Kalman Filter estimates the relative offset between two of its vertices. An additional relative measurement is derived by matching point clouds obtained by a light detection and ranging (LiDAR) system. In order to yield a significant speed-up, scan matching is implemented on the GPU. To reduce the uncertainty of either the state or the map estimate, we present an approach to actively control the system resting on an extended representation of uncertainty in the map. Furthermore, it adapts its behavior depending on the current uncertainty distribution in order to find a dynamic trade-off between exploitation (improve localization performance) and exploration (improve knowledge about the environment). Finally, we present a post-processing approach to discover landing sites in the map estimate without prior knowledge. The evaluation is conducted in a numerical simulation, where the spacecraft explores the real 3D model of Itokawa in its actual dynamic environment. Within that simulation, we use a shader-based GPU implementation for simulating LiDAR measurements. We evaluate the performance of the active SLAM approach and demonstrate that the use of the adaptive approach improves navigation and exploration performance at the same time.

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Correspondence to David Nakath.

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This work was supported by the German Aerospace Center (DLR) with financial means of the German Federal Ministry for Economic Affairs and Energy (BMWi), project “KaNaRiA” (grant No. 50 NA 1318) and project “EnEx-CAUSE” (grant No. 50 NA 1505). In addition, we gratefully acknowledge the support of NVIDIA Corporation by donating a Titan X Pascal GPU used for this research.

Appendix

Appendix

1.1 A Parameters

For the covariance matrices A, Q the standard deviation STD is given to foster readability. As the noise is modeled to affect all axes in the same way, those values can be converted into the respective matrices by, e.g., A = (STD)2I.

Table 2 Parameters used

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Nakath, D., Clemens, J. & Rachuy, C. Active Asteroid-SLAM. J Intell Robot Syst 99, 303–333 (2020). https://doi.org/10.1007/s10846-019-01103-0

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