Toward Precise Ambiguity-Aware Cross-Modality Global Self-Localization

There are significant advances in GNSS-free cross-modality self-localization of self-driving vehicles. Recent methods focus on learnable features for both cross-modal global localization via place recognition (PR) and local pose tracking, however they lack means of combining them in a complete localization pipeline. That is, a pose retrieved from PR has to be validated if it actually represents the true pose. Performing this validation without GNSS measurements makes the localization problem significantly more challenging. In this contribution, we propose a method to precisely localize the ego-vehicle in a high resolution map without GNSS prior. Furthermore, sensor and map data may be of different dimensions (2D / 3D) and modality, i.e. radar, lidar or aerial imagery. We initialize our system with multiple hypotheses retrieved from a PR method and infer the correct hypothesis over time. This multi-hypothesis approach is realized using a Gaussian sum filter which enables an efficient tracking of a low number of hypotheses and further facilitates the inference of our deep sensor-to-map matching network at arbitrarily distant regions simultaneously. We further propose a method to estimate the probability that none of the currently tracked hypotheses is correct. We achieve successful global localization in extensive experiments on the MulRan dataset, outperforming comparative methods even if none of the initial poses from PR was close to the true pose. Due to the flexibility of the approach, we can show state-of-the-art accuracy in lidar-to-aerial-imagery localization on a custom dataset using our pipeline with only minor modifications of the matching model.

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Robust Stereo Visual SLAM for Dynamic Environments With Moving Object

The accuracy of localization and mapping of automated guided vehicles (AGVs) using visual simultaneous localization and mapping (SLAM) is significantly reduced in a dynamic environment compared to a static environment due to incorrect data association caused by dynamic objects. To solve this problem, a robust stereo SLAM algorithm based on dynamic region rejection is proposed. The algorithm first detects dynamic feature points from the fundamental matrix of consecutive frames and then divides the current frame into superpixels and labels its boundaries with disparity. Finally, dynamic regions are obtained from dynamic feature points and superpixel boundaries types; only the static area is used to estimate the pose to improve the localization accuracy and robustness of the algorithm. Experiments show that the proposed algorithm outperforms ORB-SLAM2 in the KITTI dataset, and the absolute trajectory error in the actual dynamic environment can be reduced by 84% compared with the conventional ORB-SLAM2, which can effectively improve the localization and mapping accuracy of AGVs in dynamic environments.

*Published in the IEEE Vehicular Technology Society Section within IEEE Access.

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