The KITTI Vision Benchmark Suite

Method

TransLO [TransLO]
[Anonymous Submission]

Submitted on 23 Oct. 2022 08:22 by
[Anonymous Submission]

Running time:		0.08 s
Environment:		GPU @ >3.5 Ghz (Python)

Method Description:

We propose a novel point transformer odometry. Instead of directly applying transformer to points, our network can process tens of thousands of points simultaneously by projecting points onto a 2D surface and then feeding them into a local
transformer with linear complexity. Specifically, it is mainly composed of two components: Window-based Masked transformer with Self Attention (WMSA) to capture long-range dependencies; Masked Cross-Frame Attention (MCFA) to associate two frames and predict pose estimation.

Parameters:

Latex Bibtex:

Detailed Results

From all test sequences (sequences 11-21), our benchmark computes translational and rotational errors for all possible subsequences of length (5,10,50,100,150,...,400) meters. Our evaluation ranks methods according to the average of those values, where errors are measured in percent (for translation) and in degrees per meter (for rotation). Details for different trajectory lengths and driving speeds can be found in the plots underneath. Furthermore, the first 5 test trajectories and error plots are shown below.