Locus: LiDAR-based Place Recognition using Spatiotemporal Higher-Order Pooling (2011.14497v3)

Abstract: Place Recognition enables the estimation of a globally consistent map and trajectory by providing non-local constraints in Simultaneous Localisation and Mapping (SLAM). This paper presents Locus, a novel place recognition method using 3D LiDAR point clouds in large-scale environments. We propose a method for extracting and encoding topological and temporal information related to components in a scene and demonstrate how the inclusion of this auxiliary information in place description leads to more robust and discriminative scene representations. Second-order pooling along with a non-linear transform is used to aggregate these multi-level features to generate a fixed-length global descriptor, which is invariant to the permutation of input features. The proposed method outperforms state-of-the-art methods on the KITTI dataset. Furthermore, Locus is demonstrated to be robust across several challenging situations such as occlusions and viewpoint changes in 3D LiDAR point clouds. The open-source implementation is available at: https://github.com/csiro-robotics/locus .

Overview of the Locus Framework for LiDAR-based Place Recognition

The paper "Locus: LiDAR-based Place Recognition using Spatiotemporal Higher-Order Pooling" introduces a novel methodology for place recognition utilizing 3D LiDAR point clouds in expansive environments. This work is significant in the domain of autonomous robotics and vehicle navigation, where accurate localization is critical for trajectory estimation and mapping. The Locus framework is predicated on integrating topological and temporal information with scene components to bolster the robustness and discrimination of place descriptors.

Key Contributions

The core contribution of this paper is a method that uses multi-level feature representations to improve the consistency and reliability of place recognition vis-à-vis 3D point clouds. The Locus framework is particularly noteworthy for the introduction of second-order pooling, along with a non-linear transformation, to generate a fixed-length global descriptor that is invariant under permutations. This is crucial for achieving viewpoint invariance—a common challenge in LiDAR-based recognition.

Methodology

Locus innovatively implements a segment-based representation of point clouds, circumventing the drawbacks of local and global descriptors. The approach involves extracting multiple levels of features from each point cloud frame. These include structural appearance, spatial topological relations, and temporal co-occurrences. The spatial and temporal relationships between segments are encoded using a directed kNN graph and a temporal pooling mechanism, respectively.

The method uses second-order pooling to aggregate these multi-level features into a holistic descriptor. A Power-Euclidean transform further refines this descriptor, significantly augmenting its discriminative capacity. The process ensures that the final global descriptor remains computationally feasible with a fixed dimension, optimizing it for real-time applications.

Numerical Results and Evaluation

Empirical evaluations conducted on the widely-used KITTI dataset underscore the efficacy of Locus, which outperforms existing state-of-the-art alternatives. Notably, Locus demonstrates resilience in scenarios characterized by viewpoint variation and occlusions—conditions simulating real-world LiDAR sensor challenges. The framework displayed a marked robustness, with a minimal degradation in F1 scores under extensive occlusion and rotational transformations.

Implications and Future Directions

The introduction of Locus heralds substantive progress in the development of resilient LiDAR-based place recognition systems. From a practical standpoint, its application could enhance the performance of SLAM systems, improving accuracy in loop closure detection and global data association. Theoretical implications underscore the merit of embedding multi-level features and higher-order statistics to manage the intricate variations encountered in dynamic environments.

Future work may explore the integration of semantic segmentation, potentially refining the granularity of the topological features used in the descriptor. Another promising avenue lies in optimizing the computational overhead associated with spatiotemporal feature pooling, to facilitate deployment in systems with stringent real-time constraints.

In conclusion, the Locus framework represents a noteworthy advancement in LiDAR-based place recognition, addressing several prevailing limitations and paving the way for enhanced SLAM performance in autonomous systems.