The rapid developments of mobile robotics and autonomous navigation over the years are largely empowered by public datasets for testing and upgrading, such as SLAM and localization tasks. Impressive demos and benchmark results have arisen, indicating the establishment of a mature technical framework. However, from the view point of real-world deployments, there are still critical defects of robustness in challenging environments, especially in large-scale, GNSS-denied, textural-monotonous, and unstructured scenarios. To meet the pressing validation demands in such scope, we build a novel challenging robot navigation dataset in a large botanic garden of more than 48000m2. Comprehensive sensors are employed, including high-res/rate stereo Gray&RGB cameras, rotational and forward 3D LiDARs, and low-cost and industrial-grade IMUs, all of which are well calibrated and accurately hardware-synchronized. An all-terrain wheeled robot is configured to mount the sensor suite and provide odometry data. A total of 32 long and short sequences of 2.3 million images are collected, covering scenes of thick woods, riversides, narrow paths, bridges, and grasslands that rarely appeared in previous resources. Excitedly, both highly-accurate ego-motions and 3D map ground truth are provided, along with fine-annotated vision semantics. Our goal is to contribute a high-quality dataset to advance robot navigation and sensor fusion research to a higher level.

Due to the complicated procedure and costly hardware, Simultaneous Localization and Mapping (SLAM) has been heavily dependent on public datasets for drill and evaluation, leading to many impressive demos and good benchmark scores. However, with a huge contrast, SLAM is still struggling on the way towards mature deployment, which sounds a warning: some of the datasets are overexposed, causing biased usage and evaluation. This raises the problem on how to comprehensively access the existing datasets and correctly select them. Moreover, limitations do exist in current datasets, then how to build new ones and which directions to go? Nevertheless, a comprehensive survey which can tackle the above issues does not exist yet, while urgently demanded by the community. To fill the gap, this paper strives to cover a range of cohesive topics about SLAM related datasets, including general collection methodology and fundamental characteristic dimensions, SLAM related tasks taxonomy and datasets categorization, introduction of state-of-the-arts, overview and comparison of existing datasets, review of evaluation criteria, and analyses and discussions about current limitations and future directions, looking forward to not only guiding the dataset selection, but also promoting the dataset research.

Interpretability of intelligent algorithms represented by deep learning has been yet an open problem. We discuss the shortcomings of the existing explainable method based on the two attributes of explanation, which are called completeness and explicitness. Furthermore, we point out that a model that completely relies on feed-forward mapping is extremely easy to cause inexplicability because it is hard to quantify the relationship between this mapping and the final model. Based on the perspective of the data space division, the principle of complete local interpretable model-agnostic explanations (CLIMEP) is proposed in this paper. To study the classification problems, we further discussed the equivalence of the CLIMEP and the decision boundary. As a matter of fact, it is also difficult to implementation of CLIMEP. To tackle the challenge, motivated by the fact that a fully-connected neural network (FCNN) with piece-wise linear activation functions (PWLs) can partition the input space into several linear regions, we extend this result to arbitrary FCNNs by the strategy of linearizing the activation functions. Applying this technique to solving classification problems, it is the first time that the complete decision boundary of FCNNs has been able to be obtained. Finally, we propose the DecisionNet (DNet), which divides the input space by the hyper-planes of the decision boundary. Hence, each linear interval of the DNet merely contains samples of the same label. Experiments show that the surprising model compression efficiency of the DNet with an arbitrary controlled precision.