147 citations · 236 across the 10 of their papers we have counts for
4 papers · 1 filter
Learning to Localize Through Compressed Binary Maps
Xinkai Wei, Ioan Andrei Bârsan, Shenlong Wang +2
One of the main difficulties of scaling current localization systems to large environments is the on-board storage required for the maps. In this paper we propose to learn to compr…
Learning to Localize Using a LiDAR Intensity Map
Ioan Andrei Bârsan, Shenlong Wang, Andrei Pokrovsky +1
In this paper we propose a real-time, calibration-agnostic and effective localization system for self-driving cars. Our method learns to embed the online LiDAR sweeps and intensity…
Pit30M: A Benchmark for Global Localization in the Age of Self-Driving Cars
Julieta Martinez, Sasha Doubov, Jack Fan +4
We are interested in understanding whether retrieval-based localization approaches are good enough in the context of self-driving vehicles. Towards this goal, we introduce Pit30M,…
Permute, Quantize, and Fine-tune: Efficient Compression of Neural Networks
Julieta Martinez, Jashan Shewakramani, Ting Wei Liu +3
Compressing large neural networks is an important step for their deployment in resource-constrained computational platforms. In this context, vector quantization is an appealing fr…