ID(O): Mapping Data Quantization for Bathymetric Collaborative SLAM
Qianyi Zhang, Jinwhan Kim
AI summary
Problem
Underwater acoustic communication's limited bandwidth, high latency, and packet loss severely hinder data exchange for collaborative bathymetric SLAM. Existing compression techniques fail to preserve critical seabed details at high compression ratios, degrading multi-vehicle navigation accuracy.
Approach
ID(O) compresses bathymetric maps into an index map, central depth map, and optional orientation map using a generalized vector quantization codebook, with orientation estimation during restoration. This compressed data is integrated into the TTT CSLAM framework to enable efficient, appearance-based loop closure detection over acoustic modems.
Key results
- ~40% higher restoration accuracy than PCA baselines
- Matches lossless compression in mapping accuracy and efficiency
- Robust to 40% packet loss and significant dead reckoning drift
- First VQ method and CSLAM system tested in a real underwater acoustic network
Why it matters
Enables scalable, high-accuracy multi-AUV seabed mapping in bandwidth-constrained underwater environments where traditional communication fails.
Abstract
Underwater acoustic communication, characterized by limited bandwidth, high latency, and low reliability, poses sig- nificant challenges for data exchange in bathymetric collaborative simultaneous localization and mapping (CSLAM). In this article, we introduce a novel vector quantization (VQ) method called ID(O) for mapping data compression in bathymetric CSLAM. ID(O) encodes the map into an index map (I), a central depth map (D), and an orientation map (O). To accommodate strict communication constraints, orientations can be partially or fully excluded from transmission, and we propose a method to estimate these orientations during map restoration. Moreover, we integrate ID(O) within a feature-based bathymetric CSLAM framework named TTT CSLAM. Extensive experiments on two large-scale sea trial datasets demonstrate that ID(O) achieves about 40% higher restoration accuracy than the baseline method using principal component analysis. TTT CSLAM with ID(O) can match that with lossless compression regarding mapping accuracy and efficiency, and it is robust against 40% packet loss and large dead reckoning drift errors across diverse environments. To the best of the authors’ knowledge, ID(O) is the first VQ method for bathymetric data compression, and TTT CSLAM with ID(O) is the first bathymetric CSLAM tested within an underwater communication network employed by acoustic modems.