Net (polyhedron)

Pattern recognition (psychology)

Mobile robot

Feature (linguistics)

Mathematics

Pipeline (software)

Lidar

Geodesy

Philosophy

Benchmark (surveying)

Artificial intelligence

Geography

Programming language

Geometry

Computer science

Linguistics

Odometry

Pose

Visual odometry

Computer vision

Remote sensing

Convolutional neural network

Robot

We present a novel deep convolutional network pipeline, LO-Net, for real-time lidar odometry estimation. Unlike most existing lidar odometry (LO) estimations that go through individually designed feature selection, feature matching, and pose estimation pipeline, LO-Net can be trained in an end-to-end manner. With a new mask-weighted geometric constraint loss, LO-Net can effectively learn feature representation for LO estimation, and can implicitly exploit the sequential dependencies and dynamics in the data. We also design a scan-to-map module, which uses the geometric and semantic information learned in LO-Net, to improve the estimation accuracy. Experiments on benchmark datasets demonstrate that LO-Net outperforms existing learning based approaches and has similar accuracy with the state-of-the-art geometry-based approach, LOAM.

LO-Net: Deep Real-Time Lidar Odometry