A Review of Visual-LiDAR Fusion based Simultaneous Localization and Mapping

Autonomous navigation requires both a precise and robust mapping and localization solution. In this context, Simultaneous Localization and Mapping (SLAM) is a very well-suited solution. SLAM is used for many applications including mobile robotics, self-driving cars, unmanned aerial vehicles, or autonomous underwater vehicles. In these domains, both visual and visual-IMU SLAM are well studied, and improvements are regularly proposed in the literature. However, LiDAR-SLAM techniques seem to be relatively the same as ten or twenty years ago. Moreover, few research works focus on vision-LiDAR approaches, whereas such a fusion would have many advantages. Indeed, hybridized solutions offer improvements in the performance of SLAM, especially with respect to aggressive motion, lack of light, or lack of visual features. This study provides a comprehensive survey on visual-LiDAR SLAM. After a summary of the basic idea of SLAM and its implementation, we give a complete review of the state-of-the-art of SLAM research, focusing on solutions using vision, LiDAR, and a sensor fusion of both modalities

CosySlam: investigating object-level SLAM for detecting locomotion surfaces

While blindfolded legged locomotion has demonstrated impressive capabilities in the last few years, further progresses are expected from using exteroceptive perception to better adapt the robot behavior to the available surfaces of contact. In this paper, we investigate whether mono cameras are suitable sensors for that aim. We propose to rely on object-level SLAM, fusing RGB images and inertial measurements, to simultaneously estimate the robot balance state (orientation in the gravity field and velocity), the robot position, and the location of candidate contact surfaces. We used CosyPose, a learning-based object pose estimator for which we propose an empirical uncertainty model, as the sole front-end of our visual inertial SLAM.We then combine it with inertial measurements which ideally complete the system observability, although extending the proposed approach would be straightforward (e.g. kinematic information about the contact, or a feature based visual front end).We demonstrate the interest of object-based SLAM on several locomotion sequences, by some absolute metrics and in comparison with other mono SLAM

CosySlam: investigating object-level SLAM for detecting locomotion surfaces

While blindfolded legged locomotion has demonstrated impressive capabilities in the last few years, further progresses are expected from using exteroceptive perception to better adapt the robot behavior to the available surfaces of contact. In this paper, we investigate whether mono cameras are suitable sensors for that aim. We propose to rely on object-level SLAM, fusing RGB images and inertial measurements, to simultaneously estimate the robot balance state (orientation in the gravity field and velocity), the robot position, and the location of candidate contact surfaces. We used CosyPose, a learning-based object pose estimator for which we propose an empirical uncertainty model, as the sole front-end of our visual inertial SLAM.We then combine it with inertial measurements which ideally complete the system observability, although extending the proposed approach would be straightforward (e.g. kinematic information about the contact, or a feature based visual front end).We demonstrate the interest of object-based SLAM on several locomotion sequences, by some absolute metrics and in comparison with other mono SLAM

CosySlam: investigating object-level SLAM for detecting locomotion surfaces

While blindfolded legged locomotion has demonstrated impressive capabilities in the last few years, further progresses are expected from using exteroceptive perception to better adapt the robot behavior to the available surfaces of contact. In this paper, we investigate whether mono cameras are suitable sensors for that aim. We propose to rely on object-level SLAM, fusing RGB images and inertial measurements, to simultaneously estimate the robot balance state (orientation in the gravity field and velocity), the robot position, and the location of candidate contact surfaces. We used CosyPose, a learning-based object pose estimator for which we propose an empirical uncertainty model, as the sole front-end of our visual inertial SLAM.We then combine it with inertial measurements which ideally complete the system observability, although extending the proposed approach would be straightforward (e.g. kinematic information about the contact, or a feature based visual front end).We demonstrate the interest of object-based SLAM on several locomotion sequences, by some absolute metrics and in comparison with other mono SLAM