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LiDAR-MIMO: Efficient Uncertainty Estimation for LiDAR-based 3D Object Detection | | Matthew Pitropov
; Chengjie Huang
; Vahdat Abdelzad
; Krzysztof Czarnecki
; Steven Waslander
; | | Date: |
1 Jun 2022 | | Abstract: | The estimation of uncertainty in robotic vision, such as 3D object detection,
is an essential component in developing safe autonomous systems aware of their
own performance. However, the deployment of current uncertainty estimation
methods in 3D object detection remains challenging due to timing and
computational constraints. To tackle this issue, we propose LiDAR-MIMO, an
adaptation of the multi-input multi-output (MIMO) uncertainty estimation method
to the LiDAR-based 3D object detection task. Our method modifies the original
MIMO by performing multi-input at the feature level to ensure the detection,
uncertainty estimation, and runtime performance benefits are retained despite
the limited capacity of the underlying detector and the large computational
costs of point cloud processing. We compare LiDAR-MIMO with MC dropout and
ensembles as baselines and show comparable uncertainty estimation results with
only a small number of output heads. Further, LiDAR-MIMO can be configured to
be twice as fast as MC dropout and ensembles, while achieving higher mAP than
MC dropout and approaching that of ensembles. | | Source: | arXiv, 2206.00214 | | Services: | Forum | Review | PDF | Favorites | |
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