Robust Change Detection Based on Neural Descriptor Fields
- URL: http://arxiv.org/abs/2208.01014v1
- Date: Mon, 1 Aug 2022 17:45:36 GMT
- Title: Robust Change Detection Based on Neural Descriptor Fields
- Authors: Jiahui Fu, Yilun Du, Kurran Singh, Joshua B. Tenenbaum, and John J.
Leonard
- Abstract summary: We develop an object-level online change detection approach that is robust to partially overlapping observations and noisy localization results.
By associating objects via shape code similarity and comparing local object-neighbor spatial layout, our proposed approach demonstrates robustness to low observation overlap and localization noises.
- Score: 53.111397800478294
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The ability to reason about changes in the environment is crucial for robots
operating over extended periods of time. Agents are expected to capture changes
during operation so that actions can be followed to ensure a smooth progression
of the working session. However, varying viewing angles and accumulated
localization errors make it easy for robots to falsely detect changes in the
surrounding world due to low observation overlap and drifted object
associations. In this paper, based on the recently proposed category-level
Neural Descriptor Fields (NDFs), we develop an object-level online change
detection approach that is robust to partially overlapping observations and
noisy localization results. Utilizing the shape completion capability and
SE(3)-equivariance of NDFs, we represent objects with compact shape codes
encoding full object shapes from partial observations. The objects are then
organized in a spatial tree structure based on object centers recovered from
NDFs for fast queries of object neighborhoods. By associating objects via shape
code similarity and comparing local object-neighbor spatial layout, our
proposed approach demonstrates robustness to low observation overlap and
localization noises. We conduct experiments on both synthetic and real-world
sequences and achieve improved change detection results compared to multiple
baseline methods. Project webpage: https://yilundu.github.io/ndf_change
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