The burgeoning demand for collaborative robotic systems to execute complex tasks collectively has intensified the research community's focus on advancing simultaneous localization and mapping (SLAM) in a cooperative context. Despite this interest, the scalability and diversity of existing datasets for collaborative trajectories remain limited, especially in scenarios with constrained perspectives where the generalization capabilities of Collaborative SLAM (C-SLAM) are critical for the feasibility of multi-agent missions. Addressing this gap, we introduce S3E, an expansive multimodal dataset. Captured by a fleet of unmanned ground vehicles traversing four distinct collaborative trajectory paradigms, S3E encompasses 13 outdoor and 5 indoor sequences. These sequences feature meticulously synchronized and spatially calibrated data streams, including 360-degree LiDAR point cloud, high-resolution stereo imagery, high-frequency inertial measurement units (IMU), and Ultra-wideband (UWB) relative observations. Our dataset not only surpasses previous efforts in scale, scene diversity, and data intricacy but also provides a thorough analysis and benchmarks for both collaborative and individual SLAM methodologies.

Simultaneous Localization and Mapping (SLAM) is pivotal in robotics, with photorealistic scene reconstruction emerging as a key challenge. To address this, we introduce Computational Alignment for Real-Time Gaussian Splatting SLAM (CaRtGS), a novel method enhancing the efficiency and quality of photorealistic scene reconstruction in real-time environments. Leveraging 3D Gaussian Splatting (3DGS), CaRtGS achieves superior rendering quality and processing speed, which is crucial for scene photorealistic reconstruction. Our approach tackles computational misalignment in Gaussian Splatting SLAM (GS-SLAM) through an adaptive strategy that enhances optimization iterations, addresses long-tail optimization, and refines densification. Experiments on Replica, TUM-RGBD, and VECtor datasets demonstrate CaRtGS's effectiveness in achieving high-fidelity rendering with fewer Gaussian primitives. This work propels SLAM towards real-time, photorealistic dense rendering, significantly advancing photorealistic scene representation.

3-D object detection is a fundamental task in the context of autonomous driving. In the literature, cheap monocular image-based methods show a significant performance drop compared to the expensive LiDAR and stereo-images-based algorithms. In this article, we aim to close this performance gap by bridging the representation capability between 2-D and 3-D domains. We propose a novel monocular 3-D object detection model using self-supervised learning and auxiliary learning, resorting to mimicking the representations over 3-D point clouds. Specifically, given a 2-D region proposal and the corresponding instance point cloud, we supervise the feature activation from our image-based convolution network to mimic the latent feature of a point-based neural network at the training stage. While state-of-the-art (SOTA) monocular 3-D detection algorithms typically convert images to pseudo-LiDAR with depth estimation and regress 3-D detection with LiDAR-based methods, our approach seeks the power of the 2-D neural network straightforwardly and essentially enhances the 2-D module capability with latent spatial-aware representations by contrastive learning. We empirically validate the performance improvement from the feature mimicking the KITTI and ApolloScape datasets and achieve the SOTA performance on the KITTI and ApolloScape leaderboard.