Ego-motion estimation is a fundamental building block of any autonomous system that needs to navigate in an environment. In large-scale outdoor scenes, 3D LiDARs are often used for this task, as they provide a large number of range measurements at high precision. In this paper, we propose a novel approach that exploits the intensity channel of 3D LiDAR scans to compute an accurate odometry estimate at a high frequency. In contrast to existing methods that operate on full point clouds, our approach extracts a sparse set of salient points from intensity images using data-driven feature extraction architectures originally designed for RGB images. These salient points are then used to compute the relative pose between successive scans. Furthermore, we propose a novel selfsupervised procedure to fine-tune the feature extraction network online during navigation, which exploits the estimated relative motion but does not require ground truth data. The experimental evaluation suggests that the proposed approach provides a solid ego-motion estimation at a much higher frequency than the sensor frame rate while improving its estimation accuracy online.

Object-based maps are relevant for scene understanding since they integrate geometric and semantic information of the environment, allowing autonomous robots to robustly localize and interact with on objects. In this paper, we address the task of constructing a metric-semantic map for the purpose of long-term object-based localization. We exploit 3D object detections from monocular RGB frames for both, the object-based map construction, and for globally localizing in the constructed map. To tailor the approach to a target environment, we propose an efficient way of generating 3D annotations to finetune the 3D object detection model. We evaluate our map construction in an office building, and test our long-term localization approach on challenging sequences recorded in the same environment over nine months. The experiments suggest that our approach is suitable for constructing metric-semantic maps, and that our localization approach is robust to long-term changes. Both, the mapping algorithm and the localization pipeline can run online on an onboard computer. We will release an open-source C++/ROS implementation of our approach.

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