DiSCO: Differentiable Scan Context With Orientation

Abstract

Global localization is essential for robot navigation, of which the first step is to retrieve a query from the map database. This problem is called place recognition. In recent years, LiDAR scan based place recognition has drawn attention as it is robust against the environmental change. In this paper, we propose a LiDAR-based place recognition method, named Differentiable Scan Context with Orientation (DiSCO), which simultaneously finds the scan at a similar place and estimates their relative orientation. The orientation can further be used as the initial value for the down-stream local optimal metric pose estimation, improving the pose estimation especially when a large orientation between the current scan and retrieved scan exists. Our key idea is to transform the feature learning into the frequency domain. We utilize the magnitude of the spectrum as the place signature, which is theoretically rotation-invariant. In addition, based on the differentiable phase correlation, we can efficiently estimate the global optimal relative orientation using the spectrum. With such structural constraints, the network can be learned in an end-to-end manner, and the backbone is fully shared by the two tasks, achieving interpretability and light weight. Finally, DiSCO is validated on the NCLT and Oxford datasets with long-term outdoor conditions, showing better performance than the compared methods. 1

MinkLoc3D-SI: 3D LiDAR Place Recognition With Sparse Convolutions, Spherical Coordinates, and Intensity

“…Scan Context [4] is a hand-crafted descriptor efficiently computed from a BEV scan representation in polar coordinates. Scan Context sparked a new family of BEV approaches, including a trained descriptor called DiSCO [31], Scan Context augmented with intensity information [32], or semantic-based extension called SSC [33]. Recently presented Scan Context++ [5] extends Scan Context, by providing metric localization on top of the existing topological localization.…”

Radar-to-Lidar: Heterogeneous Place Recognition via Joint Learning

“…Specifically, for radar scan, the ScanContext is the polar representation essentially. As for the lidar point clouds, we follow the settings in our previous research (Xu et al, 2021), in which occupied representation achieves the best performance.…”

“…Finally, we follow the process in our previous work (Xu et al, 2021) and apply Fast Fourier Transformation (FFT) to the polar bird's eye view (BEV) representation. To make the process more efficient, we only extract the informative low-frequency component using the low-pass filter, and then signatures F L and F R are generated in the frequency domain.…”

“…For the ScanContext representation, we set the size as 40 × 120 and finally, achieve the 32 × 32 low-frequency signature. Some parameters have an influence on the model performance, for example, bin sizes, and we follow the experimental settings in Xu et al (2021), which have been demonstrated to be effective and efficient. In the training session, there are more than 8,000 samples generated randomly and the batchsize is set as 16.…”

“…method (Xu et al, 2021) is implemented as another comparison, and the quadruplet loss term is used in the learning stage. DiSCO is not designed for heterogeneous place recognition, so we train two models for L2L and R2R separately and test the R2L using the signatures from these models.…”