OverlapNet: Loop Closing for LiDAR-based SLAM (2105.11344v1)

Abstract: Simultaneous localization and mapping (SLAM) is a fundamental capability required by most autonomous systems. In this paper, we address the problem of loop closing for SLAM based on 3D laser scans recorded by autonomous cars. Our approach utilizes a deep neural network exploiting different cues generated from LiDAR data for finding loop closures. It estimates an image overlap generalized to range images and provides a relative yaw angle estimate between pairs of scans. Based on such predictions, we tackle loop closure detection and integrate our approach into an existing SLAM system to improve its mapping results. We evaluate our approach on sequences of the KITTI odometry benchmark and the Ford campus dataset. We show that our method can effectively detect loop closures surpassing the detection performance of state-of-the-art methods. To highlight the generalization capabilities of our approach, we evaluate our model on the Ford campus dataset while using only KITTI for training. The experiments show that the learned representation is able to provide reliable loop closure candidates, also in unseen environments.

• The paper introduces OverlapNet, a deep neural network that predicts scan overlap and yaw angle for precise loop closure detection.
• It processes LiDAR data via lightweight spherical projections, combining depth, normals, intensity, and semantic cues to improve efficiency.
• Experimental results on KITTI and Ford datasets show superior precision and recall over state-of-the-art loop closure methods.

Overview of "OverlapNet: Loop Closing for LiDAR-based SLAM"

The paper "OverlapNet: Loop Closing for LiDAR-based SLAM" by Xieyuanli Chen and colleagues presents a novel approach addressing the loop closure problem in Simultaneous Localization and Mapping (SLAM) systems, specifically designed for autonomous vehicles using 3D LiDAR data. The proposed solution, OverlapNet, utilizes a deep neural network architecture to detect loop closures, a crucial aspect of ensuring accurate map construction over time by correcting accumulated drift.

Key Contributions and Methodology

The primary contribution of this paper is the introduction of a loop closure detection method through the use of OverlapNet, a deep neural network that harnesses relevant cues derived from LiDAR scans. The network achieves this by predicting image overlap and estimating the relative yaw angle between two separate scans. The input data to the network is derived from depth, surface normals, intensity values, and semantic class probabilities from the LiDAR data. Notably, OverlapNet employs a lightweight architecture that processes spherical projections of LiDAR scans, making it more efficient than directly handling raw point clouds.

Due to its generalization capabilities, OverlapNet was successfully trained on the KITTI odometry dataset and evaluated on a different, previously unseen corpus, namely the Ford campus dataset. This demonstrated the network's potential to propose reliable loop closure candidates in diverse environments.

Experimental Validation and Results

The effectiveness of OverlapNet was validated via experimental evaluations using sequences from the KITTI odometry benchmark, as well as the Ford campus dataset. Results indicate that OverlapNet outperformed other state-of-the-art loop closure detection methods. More specifically, OverlapNet achieved superior precision and recall in detecting loop closures, which subsequently improved mapping results in SLAM systems.

Implications and Future Directions

This research is significant as it extends the capabilities of existing SLAM systems by integrating a learned model that efficiently detects loop closures using geometric and semantic data, thus ensuring more accurate mapping. The findings hold promise not only for autonomous driving systems, where precise and reliable mapping is pivotal, but also for other domains requiring robust spatial understanding.

Future research directions could explore the incorporation of additional sensor modalities, like camera or radar data, into the OverlapNet framework to further improve robustness and accuracy in dynamic environments or under different environmental conditions. Moreover, integrating OverlapNet's loop closure detection into a broader array of SLAM systems could help clarify its applicability in scenarios outside of autonomous driving, such as robotics or outdoor exploration tasks.