Most of the state-of-the-art visual simultaneous localization and mapping (V-SLAM) algorithms perform well in structured environments. However, the assumption of a static world limits further practical application. In this paper, we present RS-SLAM, a robust semantic RGB-D SLAM system. RS-SLAM is able to detect both moving and movable objects for high accuracy localization. A semantic segmentation model is applied to recognize the dynamic objects. Specifically, the segmentation result is refined using the context information based on Bayesian update, which provides the more accurate extraction of the interested region. With the refined results, a kind of movable object detection approach is developed. In addition, an updating strategy during mapping is introduced and a 3D semantic OctoMap only with a static world is built in real-time. Our SLAM system is evaluated both on public datasets and real-world environments. The results show that RS-SLAM outperforms the accuracy of standard visual SLAM baselines and is capable of constructing a clean static semantic OctoMap in a dynamic environment.

Pedestrian trajectory prediction is crucial in driverless applications. To accurately predict the high-quality trajectory of pedestrians, it is necessary to consider the reasonable social interaction and the spatiotemporal relationships between pedestrians. Previous methods couldn’t accurately capture the social features of pedestrians in realistic congested situations and extract spatiotemporal interaction features with high computation. Therefore, in this paper, a novel prediction model is proposed, called the Social Knowledge-guided Graph Attention Convolutional Network (SKGACN), which aims to address the social interactions and the spatiotemporal relationships between pedestrians with low computational requirements. Specifically, the social knowledge-guided graph attention mechanism fully considers multiple information relative to pedestrians to capture their social interaction. For spatiotemporal interactions, an improved Temporal Convolution Network (TCN) model is use as it can parallelize the processing times to get as higher efficiency compared to traditional models. Compared to the state-of-the-art methods, we evaluate our proposed method after applying it on two public datasets (ETH and UCY). The experimental results show that our method performs better in terms of Average Displacement Error (ADE) and Final Displacement Error (FDE) metrics.

Learning to map the images acquired by a moving agent equipped with a camera sensor to motion commands for multigoal navigation is challenging. Most existing approaches are still struggling against collision avoidance, faster convergence, and generalization. In this article, a novel actor–critic architecture is presented to learn the optimal navigation policy. We introduce single-step reward observation and collision penalty to reshape the reinforcement learning (RL) reward function. The collision perception can be obtained by the reshaped reward function and treated as measurement information from the visual observation to avoid obstacles. Besides, expert trajectories are used to generate subgoals. A subgoal reward shaping is then proposed to accelerate policy learning with the expert knowledge of subgoals. In order to generate human-aware navigation policies, an observation-action consistency (OAC) model is introduced to ensure that the agent reaches the subgoals in turn, and moves toward the target. The whole training process is performed on a self-supervised RL approach, accompanied by an expert supervision signal. This method balances the exploration and exploitation, helping the proposed model to generalize to unseen goals. The training experiments on AI2-THOR show better performance and faster convergence speed, compared with the existing approaches. For the generalization capacity to unseen goals, the proposed method achieves the state-of-the-art success rate, with at least a 30% improvement of average episode collision.

Pedestrian trajectory prediction is crucial for enabling dynamic obstacle avoidance in social robots. Variational Autoencoders (VAE) have shown potential in predicting multi-modal distributions of future pedestrian trajectories. However, standards VAE struggle to generate accurate future trajectories, and existing prediction methods often overlook the relationships between pedestrian groups. This paper introduces a novel prediction model, called the Learning Autoencoder Diffusion Model (LADM) of Pedestrian Group Relationships for Multimodal Trajectory Prediction, which takes into account pedestrian group relationships, enhancing the accuracy of multimodal distribution trajectory prediction. In the LADM framework, each pedestrian is assigned to their most probable group through a learning process, and the interaction relationships between pedestrians and groups are determined using a pedestrian-group interaction module. To improve the quality of generated future trajectory distributions, we propose the Autoencoder Diffusion Model, the VAE functions as a generator and a diffusion model acts as a refiner. We evaluate our proposed method on two public datasets (ETH and UCY) and compare it to state-of-the-art methods. Experimental results demonstrate that our approach outperforms existing methods in terms of Average Displacement Error (ADE) and Final Displacement Error (FDE) metrics.

It is still a challenge to realize safe and fast autonomous driving through deep reinforcement learning. Most autonomous driving reinforcement learning models are subject to a single experience replay approach for training agents and how to improve the driving speed and safety of agent has become the focus of research. Therefore, we present an improved Double Bias Experience Replay (DBER) approach, which enables the agent to choose its own driving learning tendency. A new loss function is proposed to ameliorate the relationship between negative loss and positive loss. The proposed approach has been applied to three algorithms to verify: Deep Q Network (DQN), Dueling Double DQN (DD-DQN) and Quantile Regression DQN (QR-DQN). Compared with the existing approaches, the proposed approach show better performance and robustness of driving policy on the driving simulator, which is implemented by the Unity ML-agents. The approach makes the vehicle agent obtain better performance, such as higher reward, faster driving speed, less lane changing and more in the same training time.

Predicting how pedestrians will move in the future is crucial for robot navigation, autonomous driving, and video surveillance. The complex interactions among pedestrians make it difficult to predict their future trajectory. Previous studies have primarily focused on modeling the interaction features of all pedestrians in the scene. However, this approach often results in an abundance of irrelevant interactions and overlooks the time-dependent characteristics. To address these issues, we propose a spatio-temporal adaptive graph pooling network (STAGP) for pedestrian trajectory prediction. STAGP adopts adaptive graph pooling to explicitly model interactions between pedestrians, redundant interactions and establishing directed interactions. In addition, we utilize spatio-temporal attention to extract temporal features of pedestrian interactions. For the prediction of future trajectories, we use a time-extrapolator convolutional neural network (TXP-CNN). ETH and UCY datasets were used to evaluate STAGP. Comparing STAGP to the values, the experimental results indicate that it is competitive in terms of ADE and FDE metrics.